investment banks vs retail banks and the data science race







These are certainly interesting times for all involved in the global sphere of data science and none more so than within the world of banking. Within such a complex and diverse industry there are many moving parts across multiple sectors, making it more challenging for experts to spot trends. With this not only comes uncertainty but anticipation about the potential that lies within for many top-tier banks. However there is one trend in particular that has elevated to the forefront of data science. Investment banks are playing a game of catch-up whilst the candidate job market continues to gather pace.

Today most retail banks have mastered the art of leveraging the use of data science, by analyzing customer behavior and predicating which customers are most likely to default on credit cards; as well as identifying potentially fraudulent transactions. As a result, this provides a data science workload, driving the need for a retail bank to invest heavily in modern technology and recruit personnel.

Many of the top-tier retail banks are mirroring the technological advancements of the global insurance and management consultancy sectors, by creating data science groups housing predictive modelers and machine learning experts. These groups then act as an internal resource of intelligence; aiding data-driven decision making across the organization.

It is one thing identifying the need to invest aggressively in modern technology and personnel, although there are only a select few top-tier banks who have successfully executed such a strategy. There are only a select few top-tier banks who have successfully executed such a strategy. It is only a matter of time before others follow suit and begin to gather deeper customer intelligence, and realize the tangible, quantifiable benefits at stake for the entire organization going forward.



Growing Importance of Data Science

As for investment banks, substantial consideration must be given to fact that they currently struggle to determine what beneficial data science use-cases exist within their business model. Undoubtedly this issue will continue going forward into the next quarter.

The demise of the Wall Street boom-era left many investment banks in a perilous condition and those still standing have done remarkably well to stabilize. However only a fool would underestimate the beast that lies within investment banks, in the form of huge potential relevant to spending power and intelligent strategists. Once valid use-cases are identified and streamlined, then the hard-push will begin to catch up with retail banks.

During this state of industry flux, we at Harnham have witnessed notable developments taking place within the data science candidate market. As the demand for us to supply quality personnel intensifies, the job market has become more competitive than ever for candidates and employers alike.

This has led Harnham to open an office in New York City, as it is undoubtedly the banking & financial services stronghold of the United States. With this, comes the powerful status of being the most competitive data science job market in the world. Equally this creates a number of problems, yet a number of advantages for those operating within this space.

 

Banking on Life Long Learning

One trend in particular has seen quants, reared in the traditional banking world of Wall Street, who then seek a career path towards modern data science falter; as such a move means utilizing unknown technology and software packages.  These candidates now face increasing difficulty making this transition internally due to a lack of modern skills.

Their woes are further compounded when entering the job market, as most are leaving roles at investment banks with the same technology skill-set they acquired many years ago. With that said, all is not lost as there are an abundance of online data science courses now available at prodigious universities such as Harvard, allowing skill-short candidates an opportunity to get to grips with modern programming languages such as Python or R etc. and big data packages such as the Hadoop platform.

Furthermore the emergence of data science boot-camps such as the Metis Boot-Camp (an immersive Data Science boot-camp in New York City) and Columbia University offer a Master’s Degree in Statistics, with focus on data science and machine learning; provide an alternative option. Many job seekers have gone as far as ceasing employment to join such boot-camps. Some have gone a step further still, by going back to university and undertaking a Masters in a data science related field.

As a result of this movement in the candidate market, there is a tug-of-war taking place between those coming from the top universities with Masters or PhD qualifications and those leaving the investment banks searching for data science roles on the retail side.

 

The Winner Takes It All

Who gets hired, all boils down to a question of each individual banks requirements and which skills they deem as the most important - domain expertise and practical experience verses a highly accomplished modern data science skill-set. Those who combine the two will naturally be more competitive in their search and overall more enticing to potential employers.

From a positive perspective, despite the barriers to entry mentioned, we are right in the midst of a data science market that is actively evolving all the time. It is fast paced, dynamic, exciting and full of opportunity on all fronts.

Given the lean years we have experienced, this is an exciting market to be part of and one that will be at the forefront of a data and analytics evolution, which will grow exponentially over the next five years and beyond.

   Allister Duncan
 

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MACHINE LEARNING ENTERS BIOINFORMATICS AND ITS FUTURE IS BRIGHT

Machine Learning Enters Bioinformatics and its Future is Bright

Ever wondered how your email system knows which emails to show you and which to put in your junk or spam folder? Enter Machine Learning. It learns what you open and read and after a time can differentiate what you ignore, toss, or move to spam. Now imagine that same type of learning in the life sciences. As scientific advances move toward Data and Machine Learning to scale their knowledge, you can imagine the possibilities. After all, as you read this, trends in the life sciences, specifically with an eye toward bioinformatics showcase machine learning such as genome sequencing and the evolutionary of tree structures. Human and Machine Learning with a Common Goal There has been so much data provided over the past few decades, no mere mortal could possibly collect and analyze it all. It is beyond the ability of human researchers to effectively examine and process such massive amounts of information without a computer’s help.  So, machines must learn the algorithms and they do so in any number of ways. For the most part, it’s a comparison of what we know, or is already in a databank, with the information we have and don’t yet know. Unrecognized genes are identified by machines taught their function. The Future is Bright Machine Learning is giving other fields within the life sciences both roots and wings.  Imagine scientists being able to gain insight and learn from early detection predictions. This type of knowledge is already in play using neuroimaging techniques for CT and MRI capabilities. This is useful on a number of levels, not the least of which is in brain function; think Alzheimer’s Research, for example.  The hurdle? It isn’t the availability of such vast amounts of data, but the available computing resources. Add to that, humans will be the ones to check and counter-check validity which can in turn become more time-consuming and labor intensive than the computer’s original analysis. And it’s this hurdle which leads to a caveat emptor, or “buyer beware” of sorts. Caveat Emptor: Continue to Question Your Predictions In other words, how much can you trust the discoveries made using Machine Learning techniques in bioinformatics? The answer? Never assume. Always double check. Verify. But as you do so, know this. Work is already in progress for next-generation systems which can assess their own work.  Some discoveries cannot be reproduced. Why? Sometimes it’s more about asking the right question. Currently, a machine might look at two different clusters of data and see that they’re completely different. Rather than state the differences, we’re still working on a system that has the machine asking a different kind of question. You might think of it as a more human question that goes a bit deeper.  Imagine a machine that might say something noting the fact that some of the data is grouped together, but if different, it might say while it sees similarities, but am uncertain about these other groups of data. They’re not quite the same, but they’re close.  Machine Learning is intended to learn from itself, from its users, and from its predictions. Though a branch of statistics and computer science, it isn’t held to following explicit instructions. Like humans, it learns from data albeit at a much faster rate of speed. And its possibilities are only getting started. Want to see where Bioinformatics can take your career? We may have a role for you. If you’re interested in Big Data and Analytics, take a look at our 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 sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

DATA SCIENTISTS MOVE THE NEEDLE TO SCALE BUSINESS

Data Scientists Move the Needle to Scale Business

Today’s companies know how important it is to add Machine Learning and AI into their business, but without a plan, things can easily go sideways. Hiring Data Scientists for your business involves more than just hoping for the next Facebook or Google success. It’s about moving the needle on your own business. So, how do you do that?  Well, first you’ll want to think about what is you want from your business and from there, build a team to help get you there. We know that sometimes these two things pose challenges as well. So, where should you should begin? Ensure you have the right leadership structures in place In their role as Stakeholder, it’s important for top executives to have a clear understanding of where their business is and solid framework for where they want it to go. In other words, it’s important to ask yourself, is your business ready for that sort of growth and transition? Before you say, yes, and start looking for that unicorn employee, there’s a few things you need to know. Data Scientists aren’t magicians. They cannot wave a magic wand and make you ten times profitable overnight. Efficiency will come, but it will take time. In reality, if you don’t have the right Data sets, the right people in place, or the right backing and investment, then it will be a hard road to success and can lead to failed initiatives. So, how do you avoid that and get yourself set back on the right path? Here are a few key steps to consider: Don’t Put the Cart Before the Horse Understand your focus and the why of your business. Align your teams with a clear view of where you are and where you’d like to go in your business. Set clear expectations for yourself and your team.Decide What Your Team Should Look Like Ask yourself, “How much talent do I need?” Well, that depends on how much Data you’ll be working with and where you want those initiatives directed. For example, if you’re building a team just to focus on work recommendation systems, then you’ll need a far smaller team than if you were overhauling an entire platform or product line. Stop Chasing Shiny Objects Be realistic in your expectations as you build your team. So many businesses, when they think of Data Scientist, focus on the word scientist. And their first thought is they’d like to get someone with a PhD from Stanford who’s worked ten years at Google. A couple of things come to mind here, when I hear this and the first is this: When businesses first reach out, they talk to me about how they want someone from Google or Facebook or Netflix, but the reality is 9 times out of 10, you’re not going to be able to access that kind of talent.Be realistic about what sort of talent you can gain access to and ask yourself, if you could get someone from Google, Facebook, or Netflix, why would they leave that job to come work for you? What can you offer that those businesses cannot? It's important to understand the goal here is not to chase the shiny stereotypes, but to have clarity and desire to set up for success those that you do hire. For some businesses, the initial reaction is to just throw Data Scientists at a problem and believe they can fix things or move you forward faster. But like everything you need to put steps in place to get you from where you are now to where you want to be.  Learn, Grow, Pivot For most people who don’t come from a Data Scientist background, there are two schools of thought. Data Scientists are bright shiny objects who will fix all of their business problems overnight orA waste of time. To build a successful team, begin by educating those in the dark about what Data professionals are capable of and ensure everyone is aware of what is realistic. It’s important to understand that it may take six months to a year for a business to see any real outcomes. This doesn’t mean things aren’t working. It’s about investing time and not getting itchy, if something doesn’t instantly showcase results.  Rethinking Stability The gold watch after 40 years of working for the same company is a thing of the past. Good Data Scientists today, typically stay on for about 20-months, then move on to their next creative endeavor. This can be scary for a world that expects people to stay in positions for four or more years, particularly if you’re not from a tech background. However, the definition of a scientist is someone who researchers, someone who tries to find new ideas and new concepts, so these are people who are naturally inclined towards learning and towards being in new situations and these people get very, very bored very quickly. Understand that if you build your team well, people will move on and drop away. This is a good thing. It means you built a strong team and the ones who have moved on have got you to this place, now you need the next team to help you get to the next level. A constant state of flux is scary, but it can also mean your business is scaling faster, and your team of Data professionals are doing their job to move the needle. Give your team the freedom to learn, give the freedom to work on projects outside of their natural scope to be able to bring value to your business, although even within that year 18-month framework you might not see proof straight away. You need to be ok with the fact that you going to lose people. That typically means you’re doing something good.  If you’ve got a Data Scientist who is happy to just stay at your company and work on the same projects for six, seven, eight years, that’s probably a red flag; how will they keep improving? “What Got Us Here, Won’t Get Us There” The needs of your group are going to change and shift and so the people you need are going to change and shift. Again, it’s about being adaptable and being able to ride those waves and change as and when we need to. If you’re looking for more guidance in scaling your business using Data Scientists and building your Data team, Harnham can help. If you’re a candidate interested in Big Data & Analytics, 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 sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

Web Analytics Trends

Web Analytics Experts On The Year’s Biggest Trends

Size. Scale. Strategy. Metrics. The measurement of the customer experience is inherent throughout our marketing and advertising efforts in today’s world. So, we thought we’d take a step back and ask Data professionals about their thoughts on current and upcoming trends in the realm of Web Analytics.Their answers were as wide and varied as the people who gave them. But to a one, these are all in executive leadership and are exclusively focused on Web Analytics and its effects on consumer behavior. Personalization Gets More Personal  "With IoT, today service providers have a huge amount of data about their consumers. In Web Analytics, this has led to analysis of individual customer behavior, which is enabling companies to offer personalized services to their customers.  Therefore, on-page analysis has become popular because the aim here is to convert the visitor to a purchaser when the opportunity presents itself. With voice search on the rise, this has led to a push towards analyzing voices- so as to understand emotions and identify points of frustration which lead to abandoned carts."  - Avinash Chandra, Founder and CEO at BrandLoom  Privacy Focus  “Many people are concerned about how the likes of Google and Facebook handle the analytics data they collect. Google Analytics has been the default choice for most tech teams for years now, but it's being installed on fewer and fewer sites nowadays. Instead of handing your website's visitors' data to Google, many young companies want to be in control of their visitors' data.”  - Uku Tehrat, CEO at Plausible Insights Reading the Tea Leaves of Web Analytics “Access to deep information has a drawback; it’s made us reliant on the instantaneous raw data from each analytics channel to inform strategy. However, that data isn’t always accurate. To solve the problem, marketers and analysts will need to take a more holistic view of the data.  Instead of, for example, tracking revenue by channel solely in analytics, the analytics professional will be looking at the real revenue of the business and evaluating the changes in the marketing that have led to that result. By looking at the real business KPIs and creating narratives that reflect the whole of the data, an analyst will be able to avoid the short-term thinking that comes from the constant analysis of daily analytics reports.” - Doug R Thomas, Marketing Consultant at Magniventris  The Third Wave of Business Intelligence  Sean Byrnes, the CEO of Outlier, an analytics solution provider, is seeing an interesting shift in Data Analytics. This shift has us entering the third wave of Business Intelligence (BI).  The first wave, data centralization, aggregated business data in a single place, making it easy to know where to look for answers. Next, data visualization tools made extracting answers easy and accessible, allowing anyone in the organization to make use of them.  The third wave is Automated Analysis. This wave will have a big impact on data scientists and how they do their jobs. In this third wave of BI innovation, automated analysis systems will constantly examine all of a business' data and provide "curated" insights related to specific and actionable changes in the business. This is vastly different than today's dashboards, giving data scientists specific, daily direction on which parts of the business to focus on. This also helps data scientists elevate their own brand to one of a data counselor. Helping define how to use data insights to fine-tune the business.  - Sean Byrnes, CEO at Outlier. Video Marketing is Here to Stay “From a marketing perspective, the biggest trend I see moving forward is the continual rise of video consumption.  Consumers are shifting from reading blogposts to watching YouTube videos, and from reading books to watching Netflix. We know that the currency of the online world is engagement. This means that whichever large company, small business, or individual can engage their audience most effectively, wins.  When it comes to web analytics, it is important to identify the metrics that signal strong user engagement and to work to improve upon the mover time. Some of these metrics include watch time, average view duration, re-watches, returning viewers, etc. The better you can get these metrics, the more engaged your audience will be.” - Jeremy Lawlor, Co-Founder & Chief Strategist at Active Business Growth AI-Enabled Data Interpretation  “AI is constantly being mentioned as an enhanced way to interpret and help visualize data, and we are seeing various tools that promise to do that. I also see a lot of potential in using Web Analytics to predict consumer behavior and have better forecasts of performance. In general, we will have better ways to decipher them in order to better serve our needs in understanding the historical data and identifying trends and risks.”  - Gabriel Shaoolian, Founder and Executive Director at DesignRush A Three-Pronged Shift of Integration and Visualization Data connection web + mobile + offline (data integration)  DataViz development (data visualization) Development of the data-driven business model  "This shift towards a more integrated and data-driven approach should be important to business. After all, it’s the business model not just of the future, but now." - Krzysztof Surowiecki, Managing Partner at Hexe Data, a Data Analytics company A Growth Spurt   “Web Analytics should expect a growth spurt in the next decade. Technology has gotten so complicated, we need people – senior level people – with experience to understand and distill data to the business. Even something as simple as Salesforce, needs experts. Jobs aren’t going anywhere.  Businesses are desperate for educated individuals to help them make sense of all the data and filter out that which “fake” (read: bot traffic) in order to really understand the numbers and what that means for their business.”  - Daniel Levine, trends expert and keynote speaker at DanielLevine.  Web Analytics has been backstage long enough. As businesses aim to strike a balance between the data measurement of the customer journey, ensuring customers’ data privacy, and tailoring each experience, there’s plenty of work here to go around. And with the estimated growth spurt in the next decade, now’s the time to jump in. If you’re interested in Web Analytics, we may have a role for you. Check out our current vacancies or get in touch with one of our expert consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com. 

Our Interview with Amit Jnagal

Our Interview With Amit Jnagal, CEO Of AI Firm Infrrd

Amit Jnagal is the CEO of Infrrd.ai, an award-winning Artificial Intelligence software firm in San Jose. We reached out to learn what had inspired Amit to start the business and what trends he predicted for the future of the industry. In addition, we asked his take on diversity in business as well as what he looked for in prospective candidates. Here’s what he had to tell us. What inspired Infrrd.ai? It’ll be ten years next month that I started Infrrd. The same age as my son, who inspired it. Well, I’d wanted to start a venture of my own since I graduated and spent the next 12 years working for two large organizations. These experiences led to work with some exceptionally talented people. So, being a forward-thinker, I made a mental list of their names and resolved to have them work for me once my own venture took off.  I was cruising with a high-flying career when our first child was born. It was then I realized if I was ever going to start something of my own, the time was now. So, I quit my corporate job when my son was 10 days old and got to work. I’ve been an entrepreneur ever since and was successful in getting most people on my mental list to join me - some agreed in a second, some took a couple of years of convincing, a few took more than five years, and there are a few people that I am still working on. While I’ve had my share of failed ventures, this is my success and it’s taken a decade to get here.  How have trends in the industry affected your business and what do you see in the future of say, the next one to five years?  In my more than two decades of experience, I have witnessed two revolutions which have fundamentally changed the way the world works. The first was the advent of the internet and the second was smart phones.  We are at the beginning of another such revolution in AI. It amazes me still, the things we can do for our clients using AI for automation. The demand means jobs, yet at the same time, there are a few which will cease to exist, which I wrote about earlier this year.  My son, now 10-years old wants to be a pilot when he grows up. But I suspect that will be one of the jobs which ceases to exist by the time he enters the workforce. AI is here. And over the next one to five years, it’s important for businesses to know how to redesign their business around it. In order to thrive, they’ll want to be AI enabled. What are your recommendations for building a team within a startup? Where do I start? Well, I have plenty of recommendations of how to NOT to build a team within a startup. But when you’re first scaling your business, there are different phases as you start up. As you grow, you’ll need people with different specializations.  During the first few years, you’ll need people who are generalists - folks who are happy to do whatever needs to be done to move the company forward. Sales on Monday, Customer Support Management on Thursday, and throughout the week continuing to build the product. People who thrive on ever-changing responsibilities would do well in the beginning of a startup. In fact, it’s impossible to get off the ground without them.   But if they do their job right and you start to grow, a time will come when you will need specialists that can create a system for each part of the organization and scale it. This is when you start hiring separate VP of Sales, VP of Customer Success, VP of Marketing, and fine tune your leadership within your data professionals team; let them build their teams. People who get the startup started and those who scale it have very different skills and styles of working. It is important to get the right people at the right time. What are some things to consider both as a business and a candidate in regard to diversity?  Here’s an answer that you might not expect. You need to consider nothing when it comes to diversity; rather you need to 'un-consider' stuff that might cloud your judgement.  With experience, everyone starts collecting heuristics that prejudice how they look at people. More often than not, this prejudice blocks you from hiring people by 'considering' heuristics that are irrelevant. Learn to evaluate people for what they bring to the table rather than their demography.  I was surprised when we won the diversity award for 2018. I had never looked at my team using a demography lens to figure out what kind of people to hire. To our mind, as long as you have the right skill and experience to do the work, you’ll get a shot at proving yourself at Infrrd. What are companies like yours looking for in a candidate hire?  We go through a ton of profiles before we make an offer to someone. What we look for in a candidate varies by experience.  At the entry level, we look for people who have shown some spark and done something that most other people have not - creating new algorithms, getting certified in some technology, presented paper at conferences or technical events, etc.  For mid-level hires, our primary concern besides skill is whether this person exhibits our values and if he or she will gel with our team. High performers are great. But make sure they’re a good fit with the rest of your team. Remember when I said I knew how NOT to build a team? We’ve had our fair share of people and teams who just didn’t click and it was detrimental to business.  At a senior level, we look for good experience, shared values and an ability to lead people. A good leader can get outstanding results from any team. That is what makes an awesome leadership hire for us. If you’d like to learn more and are interested in working with AI. 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 sanfraninfo@harnham.com.   For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

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