Who’s your favorite team? It’s a question you’re likely to have heard greet you at an event, sports bar, or in general conversation at least once. But what is it the person really wants to know? They want to know who you are, understand where you’re coming from, and ultimately, if you will get along. When it comes to sports, though, these questions often go unasked because we’re drenched in head-to-toe gear such as hats, caps, bands, stickers, flags, cups, and other external signfiers of instant community. But what happens when Data Science and Machine Learning enter the mix? Why, you up your game of course. Die hard fans know their stats and make it a part of conversation to discuss what they would do with a player in regard to their plays, drafts, and even endorsements. Knowing stats is a competition in and of itself. Throw in the billion viewers tuned in to watch the Summer Olympics, the Super Bowl, Wimbledon, and the World Cup, and the captive audience for endorsements and advertising is out of this world. How Data Science and Machine Learning Enhance Fan Engagement Bonding with one another over stats and with advances in Big Data technology, fans and advertisers alike can create epic, interactive experiences. From Data tracking of angles to perfect the game, to knowing the best-selling food in the stands, everything is tracked and optimized for the euphoric experiences we recount into old age. Add in the cutting-edge technologies of Artificial Intelligence, Virtual Reality, and Predictive Analytics, and the Data gleaned becomes invaluable to strategists, sponsors, and fans. These analytics can help to leverage and deepen the experience while sharing insights on possible improvements. Both of these technological advances are set to change the viewers experience. Check out this article for a deeper dive as coaches explain the importance of analyzing Data. Stats and Data have been part of the sports experience since Billy Beane brought economics to baseball, but what does that have to do with sports marketing and branding? Borrowing Ideas from the Pros… of Branding and Marketing There’s no reason to reinvent the wheel. Use what the pros know to grow and scale your sports advertising. Get Local, and Get People Talking & Acting. Promote your brand locally around both national AND local events. Use the opportunity to get people talking. What do people want? What makes ‘em tick? What makes ‘em sick? What do they love about your brand and why would they tell their friends? You get the idea. Perfect for every endeavor from grass roots on up.Cross Channels. Get social. Invest in YouTube, Facebook, Instagram, Twitter, and so on. Use sweepstakes and giveaways, contests and surveys, to get people talking about you. Drive people to your site via an app or campaign which you know resonates with your die-hard fans. Use streaming, live, and exclusivity to ratchet interest.Think Long-Term. To truly see your numbers and know if what you’re doing is working takes time. This is a long game to master as you build awareness, set your objectives, and determine what parameters to set to realize success. These are just a few brand marketing ideas to scale from sports advertising. Now, it’s time to get creative. Use your giveaways as an opportunity to interact with fans and find out what they want. Well, other than their favorite team to win, of course. You Can Only Get Out What You Put In The value of Data & Analytics in modern sports is more than stats. Data Science and Machine Learning allow fans to forge deeper and more meaningful relationships with their favorite sports teams. These insights lead toward a stronger appreciation of what’s happening in the industry and with their fan favorites. Want to get in on the game? Degrees abound in the realm of sports analytics from Syracuse to Oregon University and nearly every state in between. Check this out to see where this industry can take you. For those in the Northwest, Oregon has become almost another Silicon Valley. But don’t let its rural areas fool you. They’ve got an ace in the hole from the East and is already under the moniker Oregon rural tech hub. Check out our current vacancies for additional opportunities. Contact one of our recruitment consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to firstname.lastname@example.org. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to email@example.com.
17. April 2019
The days of generically using Data in Marketing are on the decline as information gathering becomes more laser focused. Once the domain of third-party sites looking to build partnerships and boost campaign efforts, Data Management Platforms (DMPs) are making way for Customer Data Platforms (CDPs). Under the weight of data breaches, GDPR, and other security issues, technologies that center around consent are helping build consumers into campaign strategies. This is a reversal from older strategies to build data from historic data profiles at channel level. Data is Data, So What’s the Difference? In a nutshell, DMPs act as “equalizers” – all companies have access to predefined parameters of data. CDPs are “differentiators” – each customer is unique with information driven from first-party historical and contextual data, determined along the customer’s purchase journey. Tailored customer data has now gone beyond the scope of Data Management Platforms and offers marketing insights nearly unheard of just a few years ago. Now, we can understand our customers not only from their historical, factual, and contextual data within parameters we’ve created, but we can gain an outside perspective as well. Five Reasons CDPs are on the Rise Single, Unified StorageThey store ALL data (1st, 2nd, and 3rd) such as names, addresses, emails, etc. as well as cookie IDs and tags. This storage capacity makes it easy to aggregate everything into one place and integrate with advertising systems.They capture data at a granular level. This includes considering long-term storage and multiple storage formats without predefined parameters focused only on advertising – what we want to sell you, not what do you want to buy?They aren’t restricted to stages of the customer journey, but can pick up data anywhere along the customer lifecycle. This information can be used for look-alike modeling or can be retargeted for more effective advertising and marketing efforts.They create a holistic overview of customer behaviors opening up new opportunities for personalization. Customer First Brand Targeting Today’s advertising and marketing strategies are being re-evaluated to prioritize the customer experience. Once customer data had to fit into predefined boxes of information, now our data collection efforts are as unique as every person who visits your website, clicks a call-to-action, or visits a brick-and-mortar store. In a Nutshell… This is a crucial time in the industry and any door which will help us resolve issues and keep the customer front and center will strengthen our efforts. However, as we go deeper into what the customer likes and why we must keep our customer’s privacy safe whilst retaining an advantage over our competitors. DMPs, once the singular domain of marketers, was used to package and repackage data to better understand customers and improve ad targeting. CDPs, on the other hand, focus on every aspect, every angle of marketing to make advertising more customer-centric and laser-focused. Ultimately, as CDPs are all inclusive, they have absorbed Data Management Platforms into their systems, helping move the industry another step forward. If you’re interested in Data & Analytics, consumer behavior, and are actively seeking an opportunity to dig in your heels and get set up for a strong career path, we may have a role for you. Check out our current vacancies or contact one of our expert consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to firstname.lastname@example.org. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to email@example.com.
11. April 2019
As startups, FinTech, InsTech, and other industries shake up the status quo, it’s more important than ever for the more established institutions to break out of their comfort zones. Break out, buck up, and keep up with those leading the pack in digital transformation. Personal data, privacy regulations, and password protections are just a few of things a Credit Risk team must consider when planning their Risk Management strategies. I spoke to Ewan Dunbar from our UK office about what businesses and candidates today need to know to stay on top of their game. Here’s what he had to say when I asked what the top three roles to consider in the industry were and how they worked together? Top 3 Roles in a Credit Risk Team Process Analyst - helps to identify, design, and monitor daily processes to ensure customer accounts are efficient and effectiveData Modeler – helps to segment large amounts of data into micro and macro trends using statistical analysis. This is where solid programming experience comes in such as R and Java, though SAS is still used in older organizations, it’s being used less so as new tech startups and innovators arise. Decision Science Analyst – This role sets the wider parameters of the company’s goals using quantitative measures, then drills down to determine the best possible course of action. How Do These 3 Roles Work Together? Let’s say a customer wishes to open a bank account. The initial paperwork to be filled out and filed, entered into the system, and monitored through its lifecycle would fall to the Process Analyst. Now, the customer wishes to apply for a credit card. Here, the Data Modeler is responsible for creating a scorecard model to predict, monitor, and evaluate the customer’s ability to make timely payments. The Decision Analyst is the relationship manager who has laid out the overarching goals and following facts, variables, and other data-driven insights communicates and translates the information in a clear manner. What Kind of Education Should I Have? Big Data continues to drive growth in every industry and, by 2020, experts predict an estimated 2.7 million open jobs in Big Data and Analytics. Though it’s been touted from the rooftops for the last few years, there still remains an urgent need for qualified professionals with specific skill sets to fill the gap in these industries. And they’re not easy to find. For roles in Credit Risk, a brand name education is the name of the game. If you’re just graduating, you have a much higher chance if you come from a red brick or Ivy League background. Experience and a focus on such subjects as statistics, computer science, and mathematics are tailor-made for this industry. Beyond education, it’s also important that companies ensure their employees have opportunities to upskill in the areas they need most. Training pays for itself as companies invest in their employees. One Last Piece of Advice Find your niche. This is not a place for generalists. Once you’ve determined your focus and become an expert in your field, you’ll always be in high demand. If you’re looking to dig in your heels and get set up for a strong career path, we may have a role for you. Check out our latest Risk opportunities or contact one of our expert recruitment consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to firstname.lastname@example.org. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to email@example.com.
04. April 2019
Apartment applications. Job applications. Credit card and bank applications. We’re sharing our data today like never before and with the advent of AI and other technological advances, we’re sharing at a more rapid rate. Data breaches and unethical behaviors give us pause before we jot down our most precious information but, ultimately, there’s no stemming the tide. So, who watches out for us, the customer and the company? Enter the Risk Management Team. It All Begins with Perception In May 2018, the General Data Protection Regulation (GDPR) became law across the European Union. Its goal? To place stringent requirements on how business handles customer data. Make no mistake, however, the need and the desire is not EU-specific. It is a matter of trust and security; something customers today demand, for the most part, before signing their information away to be organized, catalogued, and analyzed. Risk teams ensure your data will be used appropriately and ensure processes for future applications. How do they do this? Risk teams need a cross-pollination of skillsets to help mitigate risk across industries. Often, risk begins in the financial sector, but it can also incorporate project management, data teams, marketing, sales, and Business Intelligence officials. And, with the advances of technology, they may also utilize Artificial Intelligence and Machine Learning to model historical data for future predictions. They must ask the right questions, ensure the right data is used for the right purpose, and validate their findings in a real-world environment. Roles of Risk Though in today’s market, everyone has a part to play, those who are focused on risk and considered part of the Risk Management Team might include the following: Chief Financial Officer (CFO) and Board Members or StakeholdersBusiness Analyst and Data Science OfficerRisk Analyst and Project ManagerStrategy and Predictive ModellerIT Marketing Together, these individuals work to challenge models, data, and decisions on behalf of customers while adhering to the company’s bottom line. Though Big Data and advanced analytics have evolved, the need to understand risks which differ in complexity, type, speed, and size remains. A few questions your Risk team might find itself asking, include: What is the impact of data and how it’s analyzed? Have we invested enough in human capital and technology, and advanced Data Analytics to focus on any potential risk including but not limited to cyber risk?Are our validations timely and appropriate? Who is responsible for decisions made by AI?Do we have the right people in place? The right tools? Are we willing and ready to challenge our data-driven and analytics-related risks?What’s our risk perspective? Do we have a good plan in place? Who will help us put one together and implement it? Ultimately, risk management in any sector, is the integration of people, processes and tools to ensure early identification and solution of risk across the enterprise. Setting the Stage or How to Get Your Risk Management Started Get buy-in from senior leadership and stakeholders as well as their commitment and dedicated participation to manage enterprise-wide risk.Make Risk Management a priority and enforce it throughout its life-cycle.Ensure technical and program management are both represented.Program management and engineering specialties should be communicated to ensure the right information is generated to help mitigate risk.Ensure risk team members, particularly those in program management, identify any concerns such as contracting, funding, costs, risks, and anything which might promote potentially dangerous ramifications if left unchecked. Even before your players are in place, you may want to consider a Risk Management Plan. Your team can help develop the parameters and implement it, but first you need to know what it is you need to watch. The CFO role in the risk team involves knowing who to pull together, what to look for, and to execute any cost-saving measures through a well-thought out plan to mitigate risk. Four Items to Consider When Creating Your Credit Risk Team As important as technological advances have become to help mitigate risk, a business still needs human capital to analyze AI decisions and offer creative solution. So, the first two items to consider when building your team may seem unusually obvious. But, the second two, may not be so clearly necessary. These included oversight and systems-wide supply chain webs of data which must be carefully tended. TechnologyHuman Capital - Get everyone on board to ensure the program’s support; Assemble the appropriate people to assess the firm’s risks; Educate your team; Set your risk level.Supply Chain - Globalization has made companies’ supply chains more vulnerable than ever. Risk Governance - Conduct a SWOT analysis (Strengths/Weaknesses/Opportunities/Threats) to help engage your company members at every level as subtly work in broader educational efforts. Want to help the 99% have access to funds they need to live the lives they want? We may have a role for you. Take a look at our latest opportunities get in touch with one of our expert consultants to find out more. For our West Coast Team, call (415) 614 - 4999 or send an email to firstname.lastname@example.org. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to email@example.com.
28. March 2019
If you’ve applied for a credit card or loan recently, you’ll be aware of the swift response you now receive. No human can crunch the numbers and make the determination that fast, right? Although big banks are now adopting Big Data, Machine Learning, and AI into their legacy processes, startups have been disrupting the sector for a few years now. As banks and credit unions scramble to keep up, Fintech innovation is bringing together machine language, analytics, and AI to help form Big Data decisions in the industry. The forward-thinking potential of these technologies has led to some real-world uses to combat fraud, offer access to alternative data sources, and suggest real-time analysis for risk. So, Robots are Determining My Credit Risk? Well, yes and no. Often, those in the financial sector are using AI to assess Credit Risk. What once required Risk Analysts to determine manually, is now done in a matter of seconds with an early warning system developed by ING, PwC, and Google. This AI-powered system helps analysts make faster and more informed decisions about potential risk. How do they do this? Using pre-set criteria, they can gauge and analyze risk based on parameters such as whether or not a client has negative media coverage or if a share price falls below a certain percentage. If the world today is based on perception, even such items as bad reviews, negative coverage, and lower than average share prices can affect determinates. In addition, having these parameters can also help determine best practices and how businesses and individuals can be given opportunities outside the scope of big bank processes. However, as data breaches continue to mar profiles of both individuals and business, Machine Learning components offer platforms the chance to stem the tide of negativity. How Machine Learning Helps Prevent Fraud This is a simple process which requires two key measures. The first is to feed the machine not just a large amount of data, but knowing the parameters set, so the machine is fed relevant information. The second is human input which gives the machine its parameters to operate by. From there, the software will take the information, gain an understanding of the data patterns, and identify any signs of fraud. If done well, the automation process will employ solutions without sacrificing quality. Machine Learning in Determining Scorecard Models Alternative data sources offer more options not only to banks and credit unions, but also to borrowers. Using Machine Learning creates a more flexible, robust model when it comes to the type of information most useful to various borrower profiles. Having profiles prepared allows for automated scorecard updates and can generate better responsiveness and intelligence of a borrower’s risk profile. This process can be empowering for both startup and big bank tech. The Matured State of Analytics Though humans must initially input parameters, the benefits of Machine Learning using a decision engine can dig deeper and reach farther than ever before. This type of platform can gather a variety of scenarios across the industry and can constantly analyze the information, helping inform the processes of setting credit limits, loan origination, and risk-based pricing. As an extension of a modern analytics platform, these processes fill in the gaps where other platforms may lack the data or programming required to run effectively. But, as these platforms mature, they are helping to drive innovation throughout the Fintech industry and shaking up the outdated, cumbersome processes of old for a much more streamlined efficient operation. Want to inform decisioning and work with data engineers to build validation frameworks? Are you looking to get in on the ground floor of a startup opportunity in the Fintech industry? If so, we may have a role for you. If you’d like to learn more, check out our current vacancies or contact one of our expert consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to firstname.lastname@example.org. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to email@example.com.
21. March 2019
What is it that makes California a mecca for the adventurous of spirit? Is it the land which sparkles gold from the goldrush years or the shiny newness of the latest in tech? From boom to bust and the promise in the dash of its life, it holds possibility in action. Or is it because, as other cities, states, and countries rally and evolve their own tech hubs, California has already settled in as the standard? The Golden State is the home of Data & Analytics. Want to see how high you can go or how to pull yourself up from a failed attempt at success? Look to the place it all began and learn how to make your insights actionable and your business decisions better. How? Begin with a platform. A Data Management Platform. You’ve Laid the Foundation, Now What? Rather than nuggets, blocks, or bars, Data gathering is cumulative. In this case, its divide, segment, and step back for the big picture; a Data Management Platform (DMP) is a unifying platform. In other words, raw Data is collated and changed into usable form. This is the core of Data-driven marketing. It is what helps businesses learn about their customers and helps to set the stage for the actionable insights that lead to happy customers. The abundance of Data can be staggering. How much of what information do you need to better manage your audience information? What do you need to know beyond the basics? How far should you drill down to shape and activate the Data you’ve been gathering and analyzing? Having the right Data reach the right customer at the right time can greatly improve a company’s bottom line. In layman’s terms, with a DMP as part of your marketing strategy, you’ll get the most bang for your buck. Making Connections Omni and multi-channel sources such as online, offline, and mobile are woven into the connections of DMPs. Unstructured Data collection is a neutral way to help marketers use their audience Data in whatever manner is best for their business. Sources come from first – and third – party sources including mobile, desktop, web analytics tools, Customer Resource Management (CRM) software, point of sale, social media, as well as the basics such as demographic and historical behavioral Data. Getting Started Organization – Determine how you want to define your Data so you can understand it when considering a DMP. How will you segment the information you’ve decided to collect?Segmenting and audience building – Once you’ve decided what information you want to gather, you can use the information to build your target audience. Imagine pinpointing a location on a map, then plotting a route to get there. Insights and audience profile reports – Here’s your chance to study the information and analyze patterns, trends, and intent. Let’s find out what exactly it is your customers want, so you can give it to them.Activation – Now, take what you’ve learned and run with it. This is the implementation phase whether it’s through advertising, messaging, even up your game and add-in the Data management platform information into your Content Management System (CMS). The possibilities are endless. Focus, Focus, Focus Here is where you’ll bring everything into focus and see just how far the possibilities can take you and your business. Below are a few ideas and things to consider: Set your audience and advertising targets – Determine the parameters for your audience’s interests and needs through the channels they most often use such as content whether audio or video.Get personal - Offer personalized experiences for web and mobile users as well as those who prefer to conduct their business offline.Game, Set, Match – When it comes to TV DMP, match your audiences on both TV devices as well as digital.Learn – Learn about your customer. Take time to get to know them online and offline through every channel available. Go deeper than point of sale information. What is it they’re looking for? What do they want? Why do they want it and how do they want to buy it? Grow – Whilst it takes a lot more time and effort to find new customers than to keep current customers happy it’s still important to use that time and effort to your advantage utilizing DMP to grow and cultivate a new audience, too. Build brand loyalty through both returning and new customers. Paid search and social – use your Data-driven audiences to target or update paid search including buys on social media. Ultimately, building a DMP will help you build a better relationship with your customers. It helps you show you have their desires at heart; and a happy customer is worth their weight in gold. Check out our current vacancies for our latest opportunities or contact one of our recruitment consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to firstname.lastname@example.org. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to email@example.com.
13. March 2019
San Francisco is both a base and a destination for tech professionals. On the edge of Silicon Valley, its uniquely small-town-big-city vibe evokes a sense of community. For better or worse. Everyone is, essentially, in the same boat. But, here’s the thing. Everyone identifies and understands what the other is going through and what they might need assistance with. Imagine being a start-up founder, CEO, or tech genius and needing to spitball, vent, or discuss projects and frustrations. Who can you turn to? Why, the bigger and more established start-ups, CEOs, and enterprising entrepreneurs, of course. The ones who have been there and done that. If you can catch them before they jet off to their next business meeting in London or Beijing. It's also home to a number of world’s best Data & Analytics events, including the World Agri-Tech Innovation Summit in March. This San Francisco summit will host over 1500 agri-food corporates, innovators, and investors in the agri-food sector. It’s theme? ‘Turning Disruptive Technology into Business Strategy through Partnership and Collaboration’ AgriTech – The Newest Frontier of Digital Transformation? It’s not that new, really. This is its fifth year. But, what it is telling is that disruptive technology is playing a large role in agriculture. Remember when scientists were trying to figure out how to make seedless watermelon? Look how far we’ve come. This summit’s focus is on sustainable agriculture and items on the menu for discussion include: Best models for successful technology commercialization. Partnerships needed to scale new technologies. How to transform the food supply chain into a more sustainable, affordable, and nutritious systems for generations (spoiler alert: sayonara high fructose corn syrup, GMOs, and additives?). Best practices and case studies of opportunities for innovation and investment. To best address the above, speakers and attendees, will consider the above within the parameters of: Automation.AI-Backed Genomics.Biological Discovery Platforms.Predictive Agriculture. Several days in the making, it seems the above is probably just the tip of the iceberg. And, from the lab to the field, greenhouses, too are transforming as Artificial Intelligence helps decrease errors in manual Data collection. Using Predictive Analytics in Agriculture In a world driven to be sustainable, and to stem the tide of overabundance generated waste, digital and analytical products in the field have moved toward these endeavors. Imagine being able to calculate how much product is needed and only growing, and cultivating that amount. Using Predictive Analytics in agriculture not only helps ensure against error, but also provides predictive modelling, Data, and Machine Learning for predicting trends in the field. Armed with this information, a more stable bottom line may be found as well as more efficient use of on-farm products. Beyond the Buzz How will analytics affect future farming and create sustainable best practices for future generations? There are quite a few predictions at the table, and the answers are helping to drive actionable insights for decisions based on Data, improving: Product decisions.Product amount.Profitability. The 3 Ps are the what. Here is the how: Mini computers in our phones grant us information from the world and, with the right applications, can tell us our stock prices, how much milk is in our fridge, and even manage the heating and cooling of our home from afar. So, what if you could check on your field before ever leaving the house? Want to get a handle on pests? How about testing your soil’s value? These are just a few of the questions being asked and answered at the summit. This is the power of predictive analytics in agriculture. The World Agri-Tech Innovation Summit is in San Francisco from March 19th and 20th. If you’re interested in Biostatistics, Bioinformatics, Computational Biology, Big Data & Analytics, we may have a role for you. We specialize in junior and senior roles. Check out our new Life Science Analytics specialism or our current vacancies for additional opportunities. Contact one of our expert consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to firstname.lastname@example.org. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to email@example.com.
28. February 2019
We are human. We are digital. We are both. The digital mindset and digital transformation, once heavily focused in marketing, advertising, finance, and retail also drives advances in Life Sciences. Computational Biology, Bioinformatics, and statistics. If you’re going to solve biological problems with data, you need Biostatistics. Just like you need a Data Engineer to create the parameters from which to build the structure of your Data, you need a Biostatistician to lay the groundwork to study the life in Life Sciences. This information can be infused in a variety of industries, not the least of which is medicine. We haven’t reached immortality yet, but we’re well on our way. Route to the Role of Biostatistician If numbers at the pixel level are your cup of tea, then this role was made for you. At its core, Biostatistics is the application of statistics to range of topics in biology. It is for the numbers geek with a creative streak, and encompasses the design of biological elements; the gathering and analyzing Data from experiments and offering solutions to problems in medicine, health, and many more. The educational component of this role is more often not at the PhD level and, as pharma works to beat the back the opioid crisis, Biostatisticians are on the rise. Not the least of which to reach out is the Food and Drug Administration (FDA), who have turned to scientists at UNC to fill knowledge gaps. Pharma may be in the news, but Biostatistics go well beyond this single focus in areas such as genetics, potential open source biological databases, and digital transformation throughout the medical fields. Want to know what else is in store for the Life Sciences? Trends to Watch The 2019 Global Life Sciences Outlook offers deeper insight into the following trends and offers a glimpse into the next wave of digital transformation with a focus on Biostatistics, Bioinformatics, and Computational Biology endeavors. Move over pharma legacy culture. There are new players in town. From tech giants diversifying into health care to small business startups controlling assets through its lifecycle, the next generation is shaking things up. The hunt for next gen meds has begun in answer to declining R&D returns making the case for strategic deal making a key innovation source for companies. Connection and integration of medical devices into existing care pathways across the Internet of Medical Things (IoMT) ecoysystem. Outsiders become insiders as increasing security risks spur companies to safeguard their data. Outsourcing expertise in AI, cognitive automation, and cloud computing for peace of mind. Cross-pollination of transformative technologies – physical, digital, and biological – to help forward thinking pharma companies evolve from pilots to determining how new technologies can best add value using:Artificial Intelligence (AI)BlockchainDIY diagnostics and virtual careInternet of Medical Things (IoMT)Software-as-a-Medical-Device (SaMD) Though only about twenty percent of organizations feel good about their place in the digital world, many remain in the experimental stage. Agile companies and the early adopters of digital technologies and platforms could benefit from deeper insights from clinical trials, better patient engagement, and faster life cycle times for products. A digital-first attitude will be a key driver of major change in the digital transformation in Life Sciences. Organizations will work toward a two-fold endeavor of divining how disruptive technologies can work together to provide value and meaningful transformation as well as putting humans back in the loop through training, retraining, or upskilling; rearranging the organization; and reconstructing how work gets done. Humans meet AI meet Machine Learning meet humans. If you’re interested in Biostatistics, Bioinformatics, Computational Biology and Big Data & Analytics, we may have a role for you. We specialize in junior and senior roles. Check out our current vacancies for additional opportunities or contact one of our recruitment consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to firstname.lastname@example.org. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to email@example.com.
21. February 2019
Boston, Massachusetts is once again on the cutting edge of medical research and technology. From Electronic Health Records (EHR) to Machine Learning and predictive modeling of healthcare best practices to Computational Biology; the final frontier of genetic editing. We have come a long way in our quest to understand and improve our quality of life. In the face of cancer research, diabetes, and liver or heart failure, the world of Computational Biology opens the scientific doors to discovery and solution. This is a place for scientists to not only get to the heart of the matter, but to the core of the problem at the cellular level. There is an old adage which states, “when pigs fly”, usually meaning some thing will never happen or is impossible. But what happens when the impossible becomes possible? The jury’s still out, but researchers are making great inroads in developing ways to save human lives using animal organs. Could Animal Organs Help Solve Donor Deficiency? There are over 100,000 patients in the U.S. waiting for a transplant operation and, for many, a this may be their only cure. Yet, our growing population and the sheer number of those waiting has created a donor deficiency of epic proportions. Researchers have been working toward successfully transplanting organs from animals into humans. Not only has their study of stem cell technology grown over the years, but with the advent of bioinformatics, statistics, and Computational Biology, a new possibility has arisen. The chance to not only transplant organs from one species to another, but using another species to host the growing of transplantable human tissue. Getting the Framework Right Computational Biology is a broad discipline honed to a fine point. Using statistical modelling, it builds a wide variety of experimental Data and biological systems to understand algorithmics, Machine Learning, automation, and robotics. Its job is to ask and answer the question of how to efficiently gather, collate, annotate, search for information. But how can it do all this to determine appropriate biological measurements and observations? At the tipping point is the notion that to truly get a good picture of the problem, the frame must be in focus. And it is this, which is the most important task for Computational Biologists to solve before continuing their research. It’s a reminder to step back and look at the problem from another angle and to challenge assumptions turning “what if” on its head. Stretching, bending, and twisting toward a solution that might not otherwise have been thought without a framework in place in order to begin modelling the system. It is in this constant learning phase, Machine Learning applications with parameters set by the biologists, in which new information is processed, analyzed, and understood. This active learning model offers opportunities for applications to learn how to learn and will play a critical role in biomedical research now and in the future. And from this place, the second biggest problem to be solved enters the equation. Now, it’s time to refine the methods of how to solve the problem. Next Steps As exciting as the possibilities are, like all things new, there are challenges. For example, not all animals will fit the bill for transplantation. The idea is to mimic as closely as possible the size and evolution of humans such as pig, sheep, or non-human primates. But, at an even finer point of challenge are our own cell’s reactions and expressions and understanding why they act the way they do. Ultimately, it’s important to be sure information at the individual cell level is inferred with statistical references to verify findings. At the pixel level, not using a fine-tooth comb could mean your conclusions are wrong. If you’re interested in Biostatistics, Bioinformatics, Computational Biology, Big Data & Analytics, we may have a role for you. We specialize in junior and senior roles. Check out our latest Computational Biology opportunities in our new Life Science Analytics specialism or our current vacancies for additional opportunities. Contact one of our recruitment consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to firstname.lastname@example.org. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to email@example.com.
13. February 2019
Just because pricing deals with numbers, it doesn’t mean it’s exclusive to the financial sector. In our last few posts, we focused heavily on the role of Pricing Analyst, what it is and how to get there. This type of analyst role is more often found in the marketing arm of many companies and might also be known as Behavior Analyst, Customer Analyst, or something similar. However, there is another type of analyst sometimes confused with Pricing Analyst which falls squarely within the Finance sector. These roles might boast titles such as Risk Analyst, Financial Analyst, or Actuary. Often, it isn’t the title that speaks to the particular strengths of one type of role over another; it is the responsibilities and skill sets documented within the job description. Like Pricing Analysts, these professionals deal with numbers and pricing. However, their focus is on models, such as those required for mergers and acquisitions or how to set health insurance premiums looking at risk. Looking for a Low to No-Risk Gig? Actuaries are in high demand. As a profession, it is one of the most diverse and tends to be more open to women and under-represented minorities. Though the focus is often on insurance and pension programs, Actuaries can find work in a number of industries including consulting firms, hospitals, banks, investment firms, and government. As advisors who manage risk portfolios while analyzing historic and current data, these professionals are business-minded people with a mathematical basis. Using mathematics, statistics, and financial theory, they analyze the financial consequences of risk. The Masonic-esque Levels of Becoming an Actuary For individuals who are numbers focused and are interested in using their data, technical, and mathematical skills coupled with business acumen; the role of Actuary might be the perfect fit. However, there are steps or levels which need to follow to enter the profession. These are exam-based and work-experience levels and your salary increase incrementally with each step. To begin, a graduate with a high GPA and one exam under their belt may find the role quite lucrative. Each exam leads to the next level and enters you into an Actuarial Society. Depending on where and what you want to practice will determine which society you’ll sit the exam: Society of Actuaries (SOA) – focus is life and health insurance, pensions, and employee benefits. Casualty Actuarial Society (CAS) – focus is automobile, fire, and liability insurance as well as worker’s compensation. American Society of Pension Actuaries (ASPA) – focus is those in the pension field, particularly in relation to federal and state governments. Each organization has its own exams and competition is fierce. Qualities sought beyond a high GPA and actuarial exam include: Good communication skills High technical ability A wide background from mathematics and statistics to the liberal arts Actuaries and analysts with an eye toward the financial and insurance sectors use their statistical skills to research, network, and connect the dots between discerned variables. The research begins with statistical modeling. Connect the Dots with Statistical Modeling In statistical forecasting models, the information gathered helps analysts make statements about real outcomes which haven’t yet come to pass. The model can then help identify what might influence these variables. An Actuary, Financial Analyst, or Risk Analyst may use a: Merger Model (M&A) – This model is most often used in investment banking and corporate development. Think mergers and acquisitions. After all, someone has to decide the value of each company, then the basis of that value once they’re merged. Complexity varies widely in this model. Budget Model – This model is used in financial planning and analysis and helps set the budget for the coming year and the years to come. Focused heavily on a company’s income, these budgets are designed on a monthly or quarterly basis. Forecasting Model – This model is used to build a forecast of the budget model. Think of it as a building block as companies structure their budget and strategies using one or a combination of these models listed. Sometimes, the forecasting and budget model are combined. Sometimes they’re kept separate. These are only three of the ten types of models used in financial planning and analysis for any number of firms and industries. But, it’s the people behind the numbers who help businesses navigate what is best for their client, customer, and bottom line. An Actuary is just one title those interested in the mathematical and statistical applications for business might find interesting. And like many of those in the Data Science field and higher tech applications, this role is in high demand. Are you the one companies are looking for? If you’re interested in finance, modeling, statistics, Big Data & Analytics, we may have a role for you. We specialize in junior and senior roles. Check out our current vacancies or contact one of our recruitment consultants to learn more. For our West Coast Team, call (415) 614-4999 or send an email to firstname.lastname@example.org. For our Mid-West and East Coast Teams, call (212) 796-6070 or send an email to email@example.com.
07. February 2019
Toys ‘R Us toppled. Sears was saved. Both of these businesses were once retail giants. What happened? Perhaps pricing had something to do with it. But, how does one determine price? Remember when you hosted garage or yard sales with your family and you spitballed a price for your sister’s doll or the coffee machine that was suddenly old now that your family had the latest model? Well, it turns out there’s a little more to Pricing Analytics than writing a number on a label and hoping someone will agree to pay it. We spoke with our own Jenni Kavanaugh, a Managing Consultant who heads up Marketing & Insight function in New York, to find out the science behind pricing. What does it entail? Who makes a good Pricing Analyst? And how does not knowing your customer affect your bottom line when it comes to a Pricing Strategy? So, What Exactly is Pricing Analytics? Pricing Analytics doesn’t seem like something you would need. But, from a company’s perspective, when you price something, it has to be at the optimal price point to make enough return whilst ensuring that demand doesn’t go down. This is where Pricing Strategy comes in. Unfortunately, it can’t come out of thin air, it has to come from somewhere data-driven. It’s actually about more than price. Other factors in pricing include the type of product, where the demand is, and what people like about the product. At What Table Does the Pricing Analyst Sit? The Pricing Analytics function usually sits in a marketing team, or with a marketing analytics team. The role is an extension of marketing because pricing is directly correlated to promotion and then to things like loyalty and coupons. It’s all derived around what’s going on with the price point. Marketing & Insights play a key role because they encompass pricing, products, loyalty, and transactions which all come together to tell us, ultimately, how customers engage with our brand. These elements tell us how customers engage with any brand. Where does Pricing Come From? Understanding purchase behaviors and your company’s target audience affects the demand for the product which also affects its price point. The question is how do we figure out what to give, how to give it, and at the most optimum price for the customer? How do we answer these questions and balance optimal pricing for our customer as well as for our bottom line? The easiest example to use is that of a retailer. How do they set the price? First, they need to answer the question, “Who are our customers?” To do this, they must conduct a bit of historic analysis. In order to dig into their customer histories, they would first need data. The data would need to be collected and analyzed before they’d need to bring in a Pricing Analyst. A company’s data team would first need to ask questions such as “What are our competitors doing?” or “What do you think is the average price for this?” Though a price point might start off at $5, it could change depending on the answers to these questions and what the data says. Once your customer base is established, and you’ve got people buying things, you need to then segment them – what we call ‘slice and dice’ – into different demographics to help you determine their purchasing behaviors. It’s all correlation using the customer audience database because what works in New York, won’t work in Chicago, and what works in Chicago, won’t work somewhere else. Ultimately, pricing is looking at what has happened and trying to make sense of why people do what they do, and based on that try to set your prices accordingly. What are Some Things to Look at When Segmenting My Customer Database? Here’s a quick checklist as you ‘slice and dice’ your own segmentations. Your goal is to understand what makes your customers buy what they buy. It’s a starting point. You can divide demographics by: Gender Age range Location What is being bought? How much of it are they buying? At what price point are they buying your products? Historic customer data is geared toward the perspective of everyday items. But, what about luxury items or new items people have never bought before. What do you when there’s no historic data from which to base your price strategy? What are some trends you’re seeing? Pricing hasn’t been too big in the U.S. as a separate function within business. Traditionally, it’s been ensconced in other titles such as Customer Analyst, Loyalty Analyst, or Buying Behavior Analyst. However, since retail isn’t doing so well here, at the moment, Pricing Analytics and understanding customer behaviors has risen to the top of the chain of importance, which makes the role quite a valuable position to fill. Over the last couple of years, there have been very few Pricing Analyst roles, but it’s important to take note that demand is growing. More often, businesses who have filled the Pricing Analyst role are more focused in qualitative companies such as Consumer Packaged Goods (CPG) companies. Think Coca-Cola or Heineken. Now they’re in stores, they need to understand how to price their products to the supermarkets to make sure they retain those vendors. In these types of companies, you don’t necessarily see someone in the role of Statistical Analyst, but when it comes to historical data, consumer behaviors, and the growing demand for pricing strategies, the roles are beginning to shift. The idea of having your own pricing team is becoming more and more prevalent. Who Would Be Most Suited to the Role? The Pricing Analyst sits within Customer Behavior. Often the role is best for those who have been involved in pricing before, or perhaps Product Analytics – these often go hand-in-hand. Product and Pricing or Loyalty and Promotion. They’re all linked together because they all involve pricing. Technical skills can take you in multiple directions. But, if you sit in a Customer Analytics team and you study customer behaviors, then you could do something like Pricing. It’s the same core concepts, the same methodology that might operate slightly differently. Or, if someone has worked on any behavioral or customer environment and have experience trying to change or improve behaviors, it’s a good place to start toward a Pricing role. Ultimately, you have to have the understanding of how customers behave before you can apply it to pricing. If you’re coming straight out of university, you want to go into a team where you get the broadest capabilities in which you might have touched on a lot of things, but you’re not an expert in one. That’s the best way to enter into it. Demand is growing for Pricing Analysts. If you think this might be right for you or if you’re interested in Big Data & Analytics, we may have a role for you. We specialize in junior and senior roles. Check out our current vacancies or contact one of our expert consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to firstname.lastname@example.org. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to email@example.com.
31. January 2019
Sales. Deals. Discounts. Price Points. Value. We are always shopping, always looking for the best price, the best deal. But, have you ever wondered who decides pricing? Though the 2018 holiday sales in the US saw a decline in spending, there still remains a whopping 84% of people shopping at any time, with the majority using their smartphones first. Navigating six categories of brands, services, and products can be overwhelming, and though most know exactly what they want to buy, they can still be swayed. We can still be swayed. Research of a product often begins on a smartphone and savvy marketers know that is the place discover what it is shoppers are looking for and how best to help them find it. The “I”s Have it in Shopping and Search The nature of shopping has morphed from the general to the specific. More and more shoppers are using natural language in their research and over 60% use “I” in their searches. The closer marketers can make their key words and phrases to natural language, the more likely shoppers’ research will bring them directly to their business. Consider how often, when we shop for travel discounts, we might say: “budget airfare” or “budget and pet friendly hotels”. No matter what you search for, or how you search for it, there still is that one person who isn’t a marketer. And it’s from their analysis of shopping habits, consumer insights, and market trends that the prices for products and services are set. The Path to Pricing Between 10 and 15 years ago, prices were set by salespeople or marketers as part of their overall responsibilities. But, today, due to a number of new pricing tools and technologies, it has become a separate job in its own right. A Deloitte survey offers deep insights into the path to pricing, but here are a few points to consider if you are considering becoming a Pricing Analyst. Educational backgrounds are as basic as business, marketing, and finance and as diverse as education, hospitality, and auto mechanics. Analytical skills Market Research Ultimately, the goal is to make sure a product or service is set at its best price. This is done by researching industry standards, competitors’ prices and their strategies, as well as market trends among consumers by: Being proactive in communicating project objectives, metrics, barriers, status, and results to every element of the organization Identifying issues and creating solutions that allow for effective price measurements.Identifying and implementing improvement projects for pricing at a strategic level. Use data to create various graphs and charts to determine if the market can bear higher rates or if they need to be lowered. This in turn drives a company’s decision of whether or not to launch new products or services. Analysts must take into consideration not only what the market will bear, but the costs associated with bringing the product to market. They’ll look at each point along the way such as research and development, manufacturing, packaging, shipping, and finally the cost of marketing the product to consumers. How to Find, Keep, and Motivate Pricing Professionals For the right person with the right skill set, the role of a Pricing Analyst is the perfect stepping stone to other opportunities. For businesses, how to find and retain talent can be found outside of first thought of avenues such as finance. Consider recruiting talent from a diversity of educational and professional backgrounds. Promote the idea pricing jobs can be a satisfying career, but can lead to other opportunities as well. Rotate people in and out of pricing fields to gain experience. When asked about their biggest challenge, over 30% of businesses said “Data availability and reliability” were their biggest challenge. Are you ready for unique field within the Data & Analytics? If you think Pricing Analyst might be right for you or If you’re interested in Big Data & Analytics, we may have a role for you. We specialize in Junior and Senior roles. Check out our current vacancies or contact one of our recruitment consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to firstname.lastname@example.org. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to email@example.com.
23. January 2019