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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The Women Who Brought Us Wi-Fi, WFH, and More

It seems almost counterintuitive to lament the statistics of women in tech when you realize how many women, throughout history, helped to build the world of technology. Some names you may know. Ada Lovelace. Hedy Lamarr. Karen Johnson. Grace Hopper.  The list is long and these few names barely scratch the surface. But you have these ladies to thank for computer programming, wi-fi, and human mathematical ‘computers’ which have paved the way for so much of what we can do today. In honor of Women's History Month, we wanted to celebrate both the women of technology who set the stage for us to work from home on computers using wi-fi to the women of tomorrow.  Women in Tech Isn’t New Near the turn of the 20th century, a ‘woman’s work’ went well beyond the kitchen. Yet, it was a housemaid who was tasked with crunching the numbers from raw Data gathered by the men of the Harvard Observatory. When the men declined to analyze their Data deeming it ‘clerical’ and therefore, women’s work, the head of the Observatory needed help. Enter Williamina Fleming, housemaid to Edward Pickering, head of the Harvard College Observatory. Williamina would go on to lead 80 ‘computers’ at Harvard. Enter The Women of ENIAC, the first computer programmers. Though the ENIAC itself was built by men, it was a unit of six women who would actually do the coding on the machine. Their calculations plotted missile trajectories on behalf of the U.S. Military. These women would go on to become mathematicians at NASA and its Jet Propulsion Laboratory. And Grace Hopper, known as the ‘mother of computing,’ helped develop the COBOL language. She also helped develop the UNIVAC I computer, the first business-focused computer. The Old Normal Would you believe the idea of working from home began with a woman bringing home her computer to write its operating systems manual? As you commute across your home from your non-work life to your working life, cup of coffee in hand, thank Mary Allen Wilkes. She’s credited with being the first person ever to have a personal computer in her home.  And there would be no working from home without wi-fi. Hedy Lamarr, the famous actress of the 1940s was also a brilliant scientist. She loved to see how machines worked and helped develop what would become wi-fi. The Pioneering Women of Today in Tech   From AI to Machine Learning to Coding, women are leading the way. Not only are their businesses data-driven, but there is a strong focus on diversity and inclusion both for human and machine. Danah Boyd, founder and president of Data & Society, is keeping an eye the on ethical and legal implications of emerging technologies. Some topics she’s focused on include accountability in machine learning and media manipulation. Want to know what’s next in the world of tech? Meet Cathy Hackl, one of LinkedIn’s Top Tech Voices and the host of the Future Insiders podcast. She focuses on AR and VR working with name brands on the how best to use these technologies.  Dr. Fei-Fei Li, co-director of Stanford's Human-Centered AI Institute is a pioneer of not only AI, but also Computer Vision. Her nonprofit, AI4ALL, is intended to improve diversity within the field. But it’s her ImageNet project which holds the most sway. The images in this database have helped ‘train’ computers how to recognize what they see. Katie Moussouris is an unlikely heroine in the world of security. Cybersecurity. In the world of Data privacy and security, we may not automatically think of a woman. But we might imagine a hacker who would use their powers for good. The founder and CEO of Luta Security, Katie Moussouris, is the best of both worlds and is busy protecting businesses and government agencies from digital threats. With a focus on diversity and inclusion in the fields of Data and Technology, Kimberly Bryant, started Black Girls Code. Her aim is to create a more diverse computer programming course. An electrical engineer herself, she was determined her daughter not feel culturally isolated or give up her passions.  These women are the tip of the iceberg of women in tech today. As a recent interviewee suggested, we encourage you, if you’re interested, to join organizations and networks that support women in data and technology fields. At Harnham, we’re proud to partner with Women in Data. If you’re interested in Big Data and Analytics, Life Sciences, Data Science, or any of our Data professional fields, 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, contact us at (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.  

CRO: Getting Customers Past Your Digital Door

Conversion Rate Optimization. CRO. If you’re an established business just getting on the technology track to improve your business, these words and acronyms can sound difficult and confusing. So, let’s put things a little simpler. Your website is your digital doorway to your business. Your service is your digital handshake. When you’re able to meet with customers face-to-face, you can get a firmer grasp on their likes and dislikes. You get to know your customers over time, they get to know you, and you begin to learn what the want so you can improve your business. If you’re a startup, you’ve opened your business because perhaps you’ve been a customer and saw a need no one could fill but you.  Whichever type of business you are, when you make changes to your website to improve your customer experience, you’ve worked through conversion rate optimization, though you may not have realized at the time. What is Conversion Rate Optimization? It is the penultimate testing strategy to convert visitors into customers. Let’s assume your eCommerce business is bringing in leads, but no one is clicking the ‘buy now’ button. If you’re wondering why, this is your chance to test your CRO through A/B testing. This kind of testing examines your original version against a change in your wording or colors. Consider the number of times you’ve seen Amazon’s logo change over the years. Today, the name is no longer needed, only the smiling arrow. The simplest of tweaks to your call-to-action (CTA), logo, colors, wording, or even a well-read or reviewed article can drive more leads for your business. Simple testing with big consequences can be overwhelming to consider. But with a few key points to consider, you may have a better focus on what you need to do. This focus will help you identify your goals, your audience, and the best conversion touchpoints for your business. What Do You Want to Optimize? Conversion means many things to many people. While ultimately the goal is to convert visitors to customers, there are a variety of ways to get there. So, what do you want to do? Do you want to have more visitors call or fill out your contact form? Do you want new subscribers to your website? Or do you want your visitors to click ‘buy now’ or ‘add to cart’? Choose one goal and work from there. Data you may already have or can gather, can offer you insight into your customers to help you know the best way to move forward. Know Your Customer Digital and Web Analytics can help you navigate the Data gathered about your customers. For example, who’s already visiting your site? How did they find you? Age, gender, and location are additional demographics which may help your team make informed decisions about what to test, why, and how it will improve your conversion rate. Bringing Your CRO Team Together There are three main roles most often brought together for conversion rate optimization. Smart businesses make CRO a part of their Marketing Strategy. So, it’s only fitting Marketing is on the list.  Marketing - These are the professionals who understand people. They know the strategy behind every level of the sales funnel within the customer journey. And from these understandings, they can troubleshoot, if needed, with acquisition, qualification, or optimization. Acquisition – These are the professionals responsible for bringing in new business. New leads. New customers. It’s their experience which can help to identify what’s optimizing well and what isn’t whether from targeting the wrong data point or on-page issues. Web Developer or Designer – These professionals assist with the technical aspects of conversion rate optimization. Begin at Your Homepage If you’re wondering where to begin, it’s best to begin at the homepage. This is where prospective customers find you and determine whether they’d like to look around a little more or not. So, knowing this there are a few things to keep in mind. ABT – Always Be Testing. This is a circular exercise in keeping up with the Jones’s of business. The more you know about your site, your goals, and your customers needs, your improvements can help to generate leads and increase sales. OTE - Optimize the Experience. When setting your goals, you’ll want to consider three goal types and set one or more. The first is to ask yourself, what do you want to happen immediately? If you want more clicks or views, this is an immediate goal. If you have a finite amount of time to generate leads, say fourth quarter of a given year, you may wish to set a campaign goal. And if you want to project net revenue or lead quality, you’ll want to set a long-term goal. Ready to optimize your conversion rate in your job search? Harnham may have a role for you. If you’re interested in the Digital Analytics, Data & Technology, or Machine Learning just to name a few, Harnham 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, contact us at (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.  

Black History Month: Ethical AI and the Bias Within

According to Brigette Hyacinth’s 2017 book entitled, The Future of Leadership, the author suggests this when considering the ramifications of AI. “Using AI to improve efficiency is one thing, using it to judge people isn’t something I would support. It violates the intention on the applications of AI. This seems to be social prejudice masquerading as science…” How often have big tech companies backtracked their facial recognition software? What are the ethical implications of moving forward and leaving AI unchecked and unregulated? 2020 was in no way a traditional year amassing change on our daily lives at near lightspeed, or so it seemed. But what was brought to bear were unrest and tensions boiled to the breaking point. And when you look at it from the perspective of AI in our daily lives. What might the world look like in another year? When Social Sciences and Humanities Meets AI “To err is human, to forgive, divine.” Humans make mistakes. Biases are unmasked with and without intent. But, when it comes to AI, those unintentional biases can have devastating consequences. From 2015 to 2019, use of AI grew by over 250 percent and is projected to boast a revenue of over $100 billion by 2025. As major businesses such as Amazon and IBM cancel and suspend their facial recognition programs amidst protests against racial inequality, some realize more than regulatory change is needed. Since 2014, algorithms have shown biases against people of color and between genders. In a recent article from Time.com, a researcher showed the inaccuracies of prediction for women of color, in particular. Oprah Winfrey, Michelle Obama, and Serena Williams skewed as male. Three of the most recognizable faces in the world and AI algorithms missed the mark. These are the same algorithm and machine learning principles used to challenge humans at strategy games such as Chess and Go. Where’s the disconnect? According to one author, it may be time to create a new field of study specific to AI. Though created in Computer Science and Computer Engineering labs, the complexities of human are more often discussed in the field of humanities. To expand further as well into business schools, race and gender studies, and political science departments. How Did We Get Here? At first blush, it may not seem comparable to consider human history with the rise of artificial intelligence and its applications. Yet it’s human history and its social construct which explains the racial and gender biases when it comes to ethics in AI. How deep seated are such biases? What drives the inequalities when AI-enabled algorithms pass over people of color and women in job searches, credit scores, or assume status quo in incarceration statistics? Disparities between rational and relational are the cornerstone from which to begin. Once again, in Hyacinth’s book, The Future of Leadership, the author tells a story of her mother explaining the community around the simple task of washing clothes. Though washing machines now exist and do allow people to do other things while the clothes are washed, there is a key element recounted by her mother washing machines lack. The benefit of community. When her mother washed clothes, it was her and her surrounding community. They gathered to wash, to visit, and connect. A job was completed, but the experience lingered on. And in the invention of a single machine, that particular bit of community was lost. But it’s community and collaboration which remind humans of their humanity. And it’s from these psychological and sociological roles, artificial intelligence should learn. Create connections between those build the systems and those who will use them.  BUILDING AI FORWARD Voices once shuttered and subjugated have opened doors to move artificial intelligence forward. It is the quintessence of ‘those who don’t know their history are doomed to repeat it’. The difference within this scientific equivalent is there is no history to repeat when it comes to technology. And so it is from the humanitarian angle AI is considered. The ability to do great things with technology is writ in books and screenplays, and so are its dangers. While it isn’t likely an overabundance of ‘Mr. Smiths’ will fill our world, it is important we continue to break out of the siloes of science versus social sciences. If AI is to help humanity move forward, it’s important to ensure humanity plays a role in teaching our machine learning systems how different we are from each other and to consider the whole person, not just their exoskeleton. If you’re interested in the Data Sciences, Data and Technology, Machine Learning, or Robotics just to name a few, Harnham 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, contact us at (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.  

Puzzle and Problem-Solvers: Software Engineers Drive Business

Software. It’s the drivers to your printer. It’s the word processor on your PC. And it’s the concept behind your productivity tools, your CRM systems, and your social media programs. Software engineers are to software what Data Engineers are to Data.  Software Engineers are the creators, builders, and maintainers of software systems and programs, so business runs smoothly. Now, that the majority of businesses have shifted online, it’s more important than ever to keep things running smoothly. These engineers must take into account not only what businesses might need to run, but also the limitations of the program. It’s a balancing act of software, hardware, limitations, and possibilities. If you took apart watches as a kid to see how they worked, Software Engineering might be for you. Are you a problem solver? Do you love putting the pieces of a puzzle together whether it’s on a board or in a crossword? Software Engineering might be for you. What Kind of Software Engineer are You? While there are a variety of roles to consider, below are some of the more popular paths taken. So, let’s say you want to build computer applications that affect what the end user sees. If you know programming languages such as Python and Java, and understand the mechanics of how to make a program work, then you may fit the classic example of a Software Engineer. If you’re more interested in the focus of robotics or automation, you may want to consider a role in Embedded Systems. You’ll still be designing, developing, and maintaining but your projects will be hardware and software used for a specific task.   Want to keep information secure? You may lean toward Security Engineer. In this role, you’ll ensure there are no security flaws. How? By operating as a ‘white-hat’ ethical hacker to attempt breaking into existing systems to identify threats. Technical Skills are Essential. Soft Skills are Important.  For anyone in the Data professions, technical skills are paramount. This not only gets your ‘foot in the door’, but ensures you know the basics. And for those who’ve been in the game a bit longer, also gives businesses confidence you can meet any challenges which may come up. Technical skills for Software Engineers include knowing programming languages like C++, Python, Java, and others like them. In this role, you’ll need to understand development processes as well as additional technical concepts. Technical skills are a standard requirement. And as important as it is to have a good portfolio and experience, you’ll want to show the business, you have the technical know-how to take on anything which may come your way. Now that cross-functional teams across departments are regular occurrences and C-suite executives are in the know, soft skills are just as important as technical skills. What are Soft Skills? In a nutshell, soft skills are communication skills. In the past, Data professionals may have been siloed away from other teams, and a liaison of sorts might have translated Data information into actionable insights. Now businesses and professionals have found it’s much more efficient to have the Engineer speak directly to their team, their leadership, or stakeholders. So, it’s imperative your soft skills are on par with your technical skills. Scope of Work for a Software Engineer According to the Bureau of Labor Statistics, Software Engineer employment growth is expected to grow 21 percent by 2028. Now that we’re working, studying, and socializing online more than ever, is it any wonder? Add to this the changing needs of organizations as they shift their practices into the cloud, and it’s more important than ever to have professionals who can design and maintain software to meet the needs of an organization. Whichever avenue you choose, whichever business you join or career path you follow, the full scope of work will be broad. You could be in charge of creating, developing, and maintaining a full product or just a single component of an app. Regardless of your scope of work, though, you’ll most likely be working with developers, cross-departmental staff, executives, clients, and stakeholders to mold, shape, and fulfill a design envisioned for their product. If you’re interested in the Data Science, Data Technology, Machine Learning, or Software Engineering, Harnham 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, contact us at (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.  

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