Meet the data scientist: Daymond Ling

By Stephanie Robertson, SAS Insights Editor

Daymond Ling, a data scientist at CIBC for the past 20 years, believes the right personal traits are more important than technical skills when it comes to being a successful data scientist. He says the latter can be learned; the former is much harder to shape and cultivate.

What’s your background and education?
Ling:
I’ve always had a fascination with how the world and universe works; things like looking for order, patterns, purity and truth. That led me to a BS degree in honors physics. I then obtained a Master of Science in business administration with a focus on statistics and operations research. I liked learning about unpredictability, chaos and a statistical approach to systems. Operations research involves learning how complex systems’ behavior can be described mathematically followed by the analysis of optimal decisions of desired states and outcomes. And actually, the learning never stops.

Daymond Ling, Data Scientist for CIBC
Daymond Ling, Data Scientist for CIBC


What skills help you most as a data scientist?
Advanced analytics requires deep technical competencies. You need to have a good grasp of mathematics and statistics. Knowledge of operations research further extends one’s ability into optimal decision making in complex trade-off problems, a very useful skill. It isn’t necessary to have degrees in these disciplines, but proficiencies are required. You need these skills to be in the advanced analytics game, they are entrance requirements, but they are not enough – I always tell people that quantitative skills are necessary but insufficient conditions for success.

Those of us working in industries are hard-nosed problem solvers. Companies hire us not because they want to have a statistician on staff; they hire us to solve problems, to create business value. You need to be curious about the business processes and how they interconnect. You need clear structured thinking to identify well-defined problems, and then have common sense approach to the solution, balancing sophistication and complexity against resource and speed of delivery.

In the world of big data that we live in today, we are faced with exponentially increasing data sizes. You need the ability to shape and manipulate very large amounts of data at will. You need to be able to manipulate the data every which way you want with absolute ease. The ability to express your analytical ideas and thoughts via crisp, clean, modular code with lightning speed is a big boost to productivity. I cringe when IT folks say analytics is just running a bunch of SQL queries. The world of numerical analysis, statistical computing and scientific calculations is totally different from conventional business-type IT programming. Just because we do it on computers does not make us programmers in the conventional sense.

And, when you have the solution to the problem, you need great storytelling abilities to help business people understand what you saw, what you did, what you found out and what they should do – all in clear, persuasive, captivating manner using their language, not your analytical language. How’s that for a challenge?!

In many ways, I believe personal traits are more important than technical skills as the latter can be taught and learned, whereas the former is much harder to shape and cultivate.

When did you figure out you wanted to be a data scientist? What motivated you to become one?
I am naturally drawn to figuring out how things work and how I can influence and shape their behavior and outcome, if that’s possible. I believe this innate curiosity to understand how things work is core to being an applied data scientist – one that solves real-world problems. This is distinctively different from those that love doing theoretical mathematical research in statistics and operations research (equally important, and complementary to the applied world).

The label “data scientist” is a relatively new term as industries strive to build awareness of advanced analytics based on data and rebrand it as a science. It isn’t so much that I wanted to be a data scientist, as one day I woke up and people started calling me a data scientist. I went by many different names prior to its recent popularity, and I have always practiced my craft and walked my path regardless of industry and business spotlight, or lack thereof.

Practitioners of data science have been honing their skills long before its recent buzz and hot marketability. For us, it’s our passion, our calling, our being. It’s who we are – not a fad. And we’ll keep doing it when the spotlight moves somewhere else, as it inevitably will.

We do need more data scientists; there are so many more problems that can be solved today than before. I would advise people to choose this field not because it sounds nice and is popular now, but because it is our passion, because it is who we are – analytical problem solvers. This is my long-winded way of saying I believe true motivation is very much innate rather than externally driven.

What department do you work in and who do you report to?
I am always attached to the business unit where the problems reside. Advanced analytics is not an IT function, and being distant from the problem creates, in my opinion, unnecessary and detrimental barriers. You cannot solve a problem well when you are distant from it.

At American Express, I worked in card operations and risk management. I analyzed client behavior across multiple departments that look after the card product life cycle, from application, authorization, credit, collections and fraud. Currently at CIBC, I am attached to marketing and report to the vice president of customer intelligence and competitive insights.

The shift from risk management to marketing is interesting. Whereas risk management is about controlling operational processes and mitigating losses, marketing brought a fresh new perspective of using data and analytics to identify business opportunities and create new possibilities. I joke with my peers in risk management that it is now my turn to create headaches for them as we look for new ways to grow the business.

How long have you had your job and were you hired specifically to be a data scientist?
I joined CIBC to establish the advanced analytics function in customer marketing when there was none, which was almost 20 years ago. So yes, I was hired specifically as a data scientist. My mandate was to build an analytics team and establish the business practice of using advanced analytics on client data to uncover insights that would allow us to compete better in the marketplace.

Do you work on a team? If so, what’s the makeup of your team?
I surround myself with the best and brightest people who have analytical passion and are staunchly problem-solving oriented. We contribute our individual talents and learn from each other. Our different experiences and perspectives enrich each other. This cross‐pollination can work wonderfully to round out people’s skills and views on solving real-world problems.

Over the years, I’ve had the pleasure to work with both experienced people as well as talent straight out of school. They are physicists, mathematicians, econometricians, statisticians, biostatisticians – all people with a strong quantitative skill set and a disciplined scientific mind. Many started knowing little about formal statistics or SAS, but their strong quantitative foundation and bright mind enabled them to pick up skills on the job. I run a training school of sorts.

What’s your job like? Is there a typical day or is each day different? Can you give us a basic idea of what you do and the kind of projects you work on?
My days are a mix of many things – working with business partners to understand their business problems and concerns, educating people on what we can do and how it can benefit them, proposing frameworks and methods to solve problems, presenting findings and driving for change. Even politely declining engagement where analytics is not what is needed.

I also spend time thinking about how to deliver better value. What problems should we solve next quarter, next year and in the next three years? And how we can solve problems better. How can we communicate better with our business partners and increase the scale of our operations with the same number of people?

In a managerial role, I watch over my team to see what they know, what they don’t know and what they need to know. I want to help them see deeper, farther and clearer, and pass on knowledge and skills to them. I want them to have fun, and of course we want to recognize people for their achievements. On the technical side, we’re constantly looking to see where the industry is going, what we should do to stay current, where we can get more relevant data and how bleeding edge we should aspire to be given what we need to do to continuously deliver results.

Specific examples of projects we work on include customer acquisition for the bank, predicting individual behaviors, determining risks and responding to changing market conditions and business strategies.

We have weekly full team meetings where all new discoveries are put on the table and probed to make them better. We cross-check what we just discovered with what we already know from before. I try to challenge people. I want them to think about how problems can be approached differently and what possibilities we have for improving on what we’ve already done. We also keep an idea log of what would be interesting to investigate – what we call our “skunk-work projects.”

Is your job what you expected it to be?
In the first phase of my career as an individual contributor, it was everything I expected it to be – working on interesting problems, honing technical skills, using SAS to get at the truth of the matter and being recognized as competent with data and analytics.

The second phase of my career is distinctively different – the stewardship of an analytics function. The analytics side continues to be what I want it to be, a beacon of light that brings out invaluable insights that businesses need to know. Creating a team, working with brilliant people and shaping its culture taught me many new things. It’s about creating what you want it to be, rather than accepting a given sandbox. Corporate politics, bureaucracy, adoption of change is not what I expected it be. Just having insights isn’t enough to cause organizational change; it takes more than that to rally change.

I’m working on the third phase of my career. I’m hoping it will shape up to be an advocate of advanced analytics – and that is to help organizations adopt the right perspective and pass on knowledge and experience to people entering the field.

What’s your biggest challenge?
I have multiple challenges:

  • Sustaining a team of top-notch advanced analytics people.
  • Obtaining more data that’s outside of our reach.
  • Equipping teams with the right tools.
  • Governance over what to solve and what not to solve; one eye on the far horizon and one eye on now, both with industry and the analytics field.
  • Driving for business change.

I suppose the last one is the most challenging for me given I can orchestrate the other factors. For other organizations, it may well be the first one. Top-notch talent is very hard to find if you want the best.

What’s your biggest accomplishment thus far?
My biggest joy is introducing advanced analytics to many bright people and instilling analytics passion within them. It’s wonderful when you see that “they get it.” The light bulb comes on, their torch is lit and it will continue to spread.

As far as technical accomplishments, I have a long list, but the one that stand out is identifying a billion dollar funds impact, a research project that hit gold. I did it in three months’ spare time. Just an idea, gobs of data and SAS – that’s all it took. It’s not every day that you can find a prize of that size.

What do you enjoy doing in your spare time?
What spare time?! I do listen to music. I practice Taichi and meditation in my down cycles. I dabble in Zen and Buddhism. I read up on a variety of topics: physics, astronomy, cosmology, statistics, operations research, computing technology, SAS blogs (Rick Wicklin!). Then I just hold them in my mind and let them incubate. It’s probably weird, but I also like to play video games. When I play, while I enjoy the game for what it is, I am also dissecting how the game was put together, the creativity and teamwork required to pull together a great interactive story using video games as the medium of expression.

These cross-interconnections help me break out of my own preconceived notions to see things more clearly for what it truly is, rather than what I imagine it to be. This process is recursive, my new awareness is still not what it truly is, so the cycle continues. My best ideas and solutions for many problems “just pop out” when I am in a quiet zone and am not actively thinking about it. I try to get into this space as often as I can because it is quiet, rejuvenating and creative.

What’s your favorite new technology or app?
The technology I admire and follow the most is the rapid advancement of affordable massive computing hardware, and the advancements in massive parallel processing. These two factors – hardware and software – allow us to do things never possible before in human history. They fuel the practice of advanced analytics on a scale unseen before.

While admiring our ability to compute, I will never turn on Skynet. The massive increase in computing power allows us to model the world like never before, but knowledge discovery is a human endeavor that is not replaceable by machines. That’s my belief anyway.

Discovery is a journey requiring art, science, vision, judgment, inspiration and (mostly) perspiration. When it all clicks together, you get to see what is in a brand new light. It is creation. It is pure magic. This is why I do what I do.

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