Danny Butvinik’s Data Science Journey

“How big is the universe? Alicia Nash asks as her face beams with curiosity and allure. “Infinite. I know because all the data points to it being infinite,” John Forbes Nash Jr. replies confidently, even though there is no evidence to support his statement. “I don’t know. not; I just believe it,” he said with a rather innocent smile.

Although Ron Howard’s A Beautiful Mind focused on Nobel Prize winner John Forbes Nash’s battle with schizophrenia, it highlighted his unique ability to see patterns where no patterns exist. He saw the world in a different light, and that was all he needed to make his mark on history.

With nearly 15 years of experience in research, development and management in the field of data science and software development, Danny Butvinik, Chief Data Scientist of NICE Actimize. NICE Actimize is a software company that helps customers fight financial crime.

At Actimize, Danny builds and manages applied research-based ethical AI within Actimize FinCrime portfolio products and support services, leveraging collective intelligence and data consortium to provide customers with solutions transparent, adaptive, and measurable analytics by reducing fraud losses, time-to-insights, and paving the way for Actimize customers and market. He plays a central role in the processes of due diligence, intellectual property development, customer engagements, business strategy and scientific research roadmaps to achieve short and long-term goals in an environment fluid. His background in the field has also earned him a reputation as a mentor, nurturing some of the brightest minds in data science and AI.

With 12 patents pending, it aims to define and implement world-class data science practices to ensure that this information is timely, robust, repeatable and trustworthy.

In an exclusive interview with Analytics India Magazine, Danny tells us about his professional journey from academia to industry.

AIM: What drew you to data science?

Danny Butvinik: My perception of data science is quite perplexing. We have digital devices everywhere: Internet, emails, social platforms, homes, streets, transport, aviation, satellites, mobiles, watches, etc. We are literally saturated with abundant digital organisms living their lives and leaving their footprints. In fact, these are not their fingerprints; these are our footprints. These devices trace, store, capture, signal, track, detect and identify whatever they are intended for. Every day we send 306 billion emails and 500 hundred million Tweets. In 2020, humanity generated 2.5 quintillion bytes of data per day. By 2025, we will generate 463 exabytes of data every day. If we step aside and look at our world and the huge amount of information that runs through the veins of the digital world, we may see or hear noise, lots of noise. That said, there are shapes, patterns and tendencies in this alleged chaos. You just need to know how to watch it. And once you do, you’ll reveal an unimaginably structured system that can deliver insights you never thought possible.

But that’s only the beginning. Once you know how to make sense of infinitely complex data systems, you can go further: predict. Once you are able to predict, the next step would be prescription. The prescription consists of being able to ask and answer the question: “What must be done to obtain a desirable result?”. All these aspects have motivated my interest in data science.

As mathematics is a language through which we can describe nature, data science is a field through which we can understand our complex, perplexing and mysterious world of cause and effect, relationships, trends and patterns.

AIM: What was your first job in this field? What were the main takeaways?

Danny Butvinik: My first job was in academia, where I was involved in multidisciplinary research. My main conclusions were that data science is like a newborn, with small steps, despite the fact that all its pillars rest on the shoulders of giants (mathematics, statistics, information theory, computer science, network theory of neurons, computer learning theory and others.)

AIM: How did you pivot into a role in the industry?

Danny Butvinik: My long journey to Chief Data Scientist started during my years at the academy, where I studied advanced statistics, information theory, computational geometry, data structures, streaming algorithms, advanced simulations and optimizations. My strong background in mathematics allowed me to explore various fields and gain valuable experiences that later shaped my skills in artificial intelligence and data science.

At some point, I decided to exploit my extensive theoretical knowledge by materializing it in the industry. Before joining NICE Actimize, I worked in several companies in different fields such as computer vision, image processing, signal processing, security and health. In addition to my solid background in mathematics, I have developed my knowledge in various scientific disciplines and cross-domain industries. It shaped my perception of data science as a discipline and paved the way for things I would like to do in the future. I have immersed myself in incremental online machine learning, online active machine learning, online reinforcement machine learning and complex AI-based systems.

Having discovered my specialization in AI and data science, I continued to explore and deepen “esoteric” subfields such as the expansion of Chaos in complex systems, the uncertainty of AI, parsimonious models, ergodic processes, causal inference, and information-based uncertainty in the decision boundary for classification problems.

After joining NICE Actimize, I realized very quickly that financial crime is the most difficult and fascinating field that I have crossed, and that it offers enormous potential for data scientists to explore. It resonates perfectly with my favorite research topics as well.

My main motivations are curiosity, enthusiasm and a voracious desire to quench the thirst for knowledge. Of course, that means a lot of hard work, reading, continuous learning, and resilience.

AIM: Tell us about Actimize and what it offers.

Danny Butvinik: NICE Actimize is the world’s leading provider of financial crime, risk and compliance solutions. Actimize has different LOBs including AML, Fraud & Authentication Management, Financial Markets Compliance, Investigation & Case Management and Data Intelligence.

We leverage machine learning and AI to detect and prevent financial crimes across the entire financial services industry, including some of the world’s largest financial institutions.

We leverage elements of decentralized AI such as federated machine learning to leverage the data consortium in cloud fraud detection and create a paradigm shift to stop real-time fraud before it happens. produce by exploring online incremental machine learning and obtaining continuously adaptable solutions for financial institutions.

AIM: As Chief Data Scientist at NICE Actimize, what are your responsibilities?

Danny Butvinik: I am the leading professional data science authority for the organization. I lead the company’s efforts in advanced analytics, autonomous financial crime and compliance. Additionally, I lead the Actimize data science community to practice building channels for collaboration, knowledge sharing, mentorship, and the continued growth of data science practice and practitioners. My team and I also work with marketing and sales managers to provide insight into customer needs and demands.

AIM: What is/was the most difficult moment professionally in your career?

Danny Butvinik: The most stimulating and, at the same time, the most interesting moment of my professional career is now. I am working with my team on online incremental machine learning research for fraud detection. For me, it is quite attractive to combine such a theoretical approach with a real implementation and integrate it into production. Of course, being enthusiastic and passionate about what I do gets me through tough times. But, at the end of the day, I believe in what I create, and that matters.

AIM: What is your personal goal as a data scientist? What do you want to accomplish?

Danny Butvinik: My short-term goals are to establish a solid theory around online incremental machine learning under certain constraints and bring it to life.

My long-term goal is to write a book that combines the field of financial crime with advanced science. I consider this book to be for a wide audience. It will contain different layers for various readers.

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