With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.
Visit our Blogs & News portal or check out our recent posts below.
Matt is the Vice President of the New York office. With over 14 years in Recruitment and Executive Search, Matt is responsible for sales growth and people development of seasoned recruitment consultants and business developers who have a deep technical knowledge and extensive local networks to support our customers in each vertical market specialism.
With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.
Visit our Blogs & News portal or check out our recent posts below.
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
Big Data tomes sit on a number of business reference shelves. Machine Learning, Analytics, and Edge Computing books compete for space in our minds, on our computers, in the cloud, and on the shelf. Over the past year, we’ve talked about the Data Scientist shortage, what Web Analytics mean to businesses, how AI will work hand-in-hand with humans and, if you’re looking for a career, how to stand out from the crowd. As the year comes to a close and we look to the new year, we wonder what 2019’s trends will be. What will change exponentially? What and who is lagging and leading? And how to navigate the soon to be third stage of ubiquitous data. Data is everywhere and, in some instances, can be too much to wade through. So, in a world of juxtapositions, the next wave of trends is to make Big Data small which will ultimately utilize AI more efficiently. Biting Off More than We Can Chew Much like the idea of music in your pocket with the introduction of the iPod, the latest trend in Big Data is to make it small, bite-sized, and navigable. So, how do you make Big Data small? The tsunami of data we encounter on a daily basis is staggering and overwhelming. As data teams become unsiloed, so too, does data. As vendors, digital leaders, business executives, and data professionals come together into a centralized team, data is being streamlined into a single view within a hub. Open source sharing, collaborating, and use of enterprise data catalogs within the hub add more value to businesses and can help to drive data management strategy. But, though education, training, and apprentice-like experiences, even the best data professionals can have trouble navigating the swathes of data they encounter each day. Enter AI. These systems are intended to cut through the data, filter the information based on algorithms it’s given and, when needed, “learn” what it needs to know to process information, and accurately share what it has discovered. From there, humans can take the information and analyze how it can be of benefit to the business and what actionable insights can, and should, be implemented. I, Human One of the more nefarious predictions of the past few years has been the fear that robots and AI would take over jobs. But, just as the dishwasher and laundry machine were developed to ease time at those chores, AI is the answer to how to increase productivity, not take over. Though AI has the capability to handle a range of tasks, it cannot replace hands-on, human-centric tasks. In retail, for example, AI might be used to make the process of shopping and buying more streamlined while freeing up the salesclerk to offer more focused customer service. A restauranteur could create the perfect ambience setting based on data about noise level, food preferences, busy vs slow times, and in so doing develop a customer base with whom they could discuss where the food comes from, offer classes, and more. AI is intended as a partnership to humans. Assisted productivity to free up time for more creative and complex pursuits. Beyond the industry executive, 2019 is predicted to be the year AI enables IT to move past routine automation tasks and proactively streamlines processes. With the assistance of AI, people will be able to work smarter, not harder, be more effective, and more productive. 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.
20. December 2018
Why Texas is the place to be for technology jobs The big data market is heating up the world over, and perhaps no more so than in Texas. The Dallas, Austin and Houston areas in particular are experiencing a massive boom in big data jobs, with many large tech companies making the move from Silicon Valley to enjoy all that Texas has to offer. But why the shift towards the southern state, and what does it mean for candidates looking big data jobs and broader technology roles? Tax-free Texas The Texan market is looking increasingly lucrative for both young start-ups and established tech companies alike. One of the most significant factors in this rapid growth is the favourable tax conditions in the state. There’s no corporate or individual income tax, with Texas ranking 47 out of 50 states when it comes to taxes paid per $1,000 of personal income. As California tax rates hitting up to 10.84 for corporations and 12.3% for individuals, it’s understandable that entrepreneurs and big business alike are looking to the southern state for bigger breaks on tax day. On top of this, Texas offers favourable funding and regulatory conditions for young and growing businesses, providing a ‘pro-business’ environment for corporations to thrive. Texas State offers billions of dollars in incentives to businesses every year, providing all the more reasons for those in the technology industry to think hard about making the move. With a state government that celebrates business and provides easy to navigate laws and regulations, many businesses find the transition from Silicon Valley to Austin smooth and seamless. As organisations in San Francisco are priced out of the area, some of the nation’s top talent are moving to pastures greener – and for many, that means Texas. The living is easy On top of the tax breaks gained when moving to Texas, many movers and shakers experience a favourable quality of life. The cost of living is low – for example, the median home value in Austin is $321,600 compared to San Francisco’s $1,1943,300 – with relatively cheap utilities and the second-largest GDP in the nation. The market is robust, which has resulted in money being poured back into cities and communities to make them more attractive to businesses and young families. People can move to Austin and get more bang for their buck than they can in many other parts of the country, enjoying not only a booming technology market, but also superior housing and affordable living. Add in a comfortable climate and famously friendly locals and you’ve got a part of the country that is becoming increasingly appealing to even the most seasoned technology professionals. Technology is taking off Texas is huge when it comes to the technology industry. There was a 41.4% jump in technology industry employment between 2001 and 2013, resulting in large numbers of jobs being taken up across Austin and the wider state. And in 2016 alone, Texas added a huge 11,000 new technology jobs to its market, ranking it second of the 50 states in tech industry employment. The tech hub of Austin alone is home to employers such as Dell, Apple, Microsoft and Samsung, plus an increasingly significant number of start-ups peppering the landscape with innovation. There are a range of incubators and universities that feed into the city’s talent pool, with Austin ranking third in the list of US cities providing the most technology jobs in 2017. However, such growth doesn’t stop Austin and its other Texan counterparts from being a friendly and accessible place to work. There is less of the cut-throat nature that comes with tech in Silicon Valley, and more of a community, collaborative approach. Meanwhile, Dallas-Fort Worth is enjoying being the second-largest data center market in the country, offering an abundance of big data jobs to savvy business people. Working in Texas Much of the Texas technology market is geared towards candidates currently, with more jobs than skilled employees to fill them. Companies are doing more to attract top talent to Texas, including offering generous benefits packages, relocation allowances and flexible work conditions, and the expectation is that this market will only continue to grow. If you’re looking for technology jobs in Austin or further afield in Texas, we might have just what you’re looking for. Take a look at our US data and technology jobs here.
05. July 2017
If your education and work experience have given you solid skills in diligence, quantitative analysis, computer software and communication, then you’re well on your way to becoming a highly valued member of the workforce. But why would a career in Credit Risk be a good choice over the myriad of other jobs that would welcome someone with those particular skills? Here are a few compelling reasons: It’s a very lucrative professionWith a huge proportion of your life taken up by work, nobody should do a job purely for the money. The monthly paycheck may look nice on your bank balance, but there’s a lot to be said for being in a job that makes you happy, not just rich. There’s no denying that a Credit Risk Analyst salary is a good one, with research from payscale.com suggesting the average salary sits at just over $80,000 with bonuses potentially adding another $10,000 to that figure. The benefits that come with that aren't to be sniffed at either. With companies competing for the best talent, they need to be come up with a range of added extras to tempt candidates including holiday allocations well above average, private medical insurance and flexible work patterns. It’s a job that’s based on dataAfter the industrial age and the technological age, now many analysts say we’re now living in the data age. Never before has it been easier to collect, collate and cross-reference information from a vast number of sources. You no longer need to rely on a basic credit report and salary history to make a sound assessment of creditworthiness. Many roles have been revolutionized by so-called ‘Big Data’ and credit risk jobs are no different. It’s a role that has always been governed by information, and the more you have at your disposal, the better decisions you’ll be able to make and the better strategies you’ll be able to put together. There’s a positive job outlookA study from Oxford University suggests Credit Analyst sits 26th on a list of roles most at risk from automation, sitting just above Parts Salespersons and below Milling Machine Setters. However, contrary to this and other reports that AI is going to make all our jobs redundant in the not-too-distant future, Credit Risk is an area that’s actually expected to grow; data from the US Bureau of Labor Statistics suggest an increase of 10% between 2012 and 2022. While computers can do an excellent job of presenting data, there’s a huge amount of human interpretation that needs to be done to deliver reasoned and thorough insight - humans may not be perfect, but nobody wants to put a potential repeat of the 2008 economic meltdown in the hands of machines. There’s an excellent career pathMany of those sitting in boardrooms in some of the country’s biggest companies will have begun their careers in Credit Risk. It’s a position that gives you an excellent insight into the interests of the company you’re working for as well as the typical applicants you’re assessing. Whether you decide to climb the credit risk ladder to more senior finance positions or branch out into sales or marketing functions, this grounding of business acumen and customer insight is sure to stand you in good stead. For the latest opportunities in Credit Risk at Harnham, take a look at our vacancies here.
28. June 2017