Climate-related challenges, from extreme weather events to global warming, can significantly impact the financial stability and long-term viability of businesses across every sector. More and more, corporations are relying on advisors to help navigate the complexities of climate change and better leverage climate related data to mitigate risks. It is within the intersection of climate change and data science that the future of corporate sustainability and financial success is being defined.

As an advisor within the Lazard Climate Center, Maxime Tchibozo (M.S. Data Science ’21) is playing a pivotal role in this area. Utilizing his experience in data science and his background in the financial industry, his mission is to enhance how corporations implement strategies for climate change mitigation and meet critical climate goals. Day to day, he helps corporations make informed decisions that not only address immediate concerns, but also encourage a deeper understanding of emerging data science research that can strengthen resilience against future threats. 

Prior to joining Columbia, Tchibozo was a distinguished student at Télécom Paris, one of France’s premier research universities, where he undertook a dual major in data science and statistical models while mastering five programming languages. In parallel to his professional commitments, he’s also enriching the academic sphere by teaching an AI and Machine Learning in Finance course at HEC Paris. Here, he equips students with the tools to harness Large Language Models and automation, enabling them to bring fresh perspectives and efficiencies to the finance and investment banking sectors.

DSI recently caught up with Tchibozo to delve into his professional journey and understand how his experiences in the M.S. in Data Science (MSDS) program equipped him to make his mark in the financial sector. 

This alumni interview series is part of our year-long celebration to mark the 10 Year Anniversary of the M.S. in Data Science Program. Follow more updates with #MSDS10.

Can you tell us more about your current role? What projects are you working on?

The Lazard Climate Center focuses on producing academic research in key areas of climate finance, such as climate policy, climate change mitigation strategies, and energy transition. One example of our work is a recent collaboration with leading faculty from Columbia University, Imperial College London, and the Harvard Kennedy School, where we co-authored a paper examining corporate emissions in the years following pledges to reduce emissions.

The Climate Center model combines the strengths of research and investment banking, and my role mainly involves translating recent findings from academia into materials that assist corporations in making strategic decisions.

I’ve noticed from firsthand experience that, above all the qualities investment bankers and researchers have in common, they are usually the first two groups to be consulted when companies and governments face crucial decisions on complex topics.

Data science, machine learning, and artificial intelligence have many important implications for the climate crisis. In light of DSI’s Data for Good motto, what impact would you say your work has in the data science community?

There is a huge skill and knowledge gap when it comes to climate finance.

On one hand, there is an overwhelming amount of information on the adverse effects of climate change; on how far behind we are in reaching stated climate goals, and on the substantial costs associated with mitigating environmental damage.

On the other hand, evaluating the benefits and costs of climate policies and solutions can be extremely challenging. Comparing climate change mitigation solutions usually requires deep technical expertise in multiple fields — whether it be reducing CO2 emissions during concrete production, estimating the environmental value of carbon-sequestering rainforests, or looking further out to technologies with long-term potential such as fusion energy.

These complexities may cause decision-makers to delay taking action.. To address this, we publish research via open access journals, and we regularly synthesize the latest insights from engineering, ecology, physics, and finance into materials that professionals from various domains can leverage.

In terms of impact on the data science community, we try to bridge the gap between economics and data science. In practice, this means finding the right level of model interpretability for stakeholders to be confident in the decisions they make. 

You’ve also a Visiting Lecturer at HEC Paris, focusing on FinTech. What are some exciting trends in FinTech that you are teaching your students?

Trends have quickly evolved. When I first gave the course on AI and Machine Learning in Finance, my biggest recommendation to students was to learn how to code. Last year, I completely changed my view, given how advanced and easy to use Large Language Models have become.

A skill that remains timeless to data science is acquiring a level of fluency in data wrangling that enables one to answer any question on any topic quickly. In the course, we collaborate on case studies of problems that none of us had thought about before, and work together to build out a data-model approach that would get us 80% of the way to a good enough solution in a reasonable amount of time. For instance, students worked out how to predict labor shortages using open-source data, and developed strategies to mitigate their impact in real- time.

How did the MSDS program help you prepare for your career? What are some lessons that you are applying at work every day?

The MSDS program gave me the right frameworks and tools to approach problem-solving at scale – which has been valuable in my current work. 

Since joining the Climate Center, I’ve leveraged algorithms from network theory to analyze power flow on the electric grid, estimated the effects of proposed climate policies using causal inference, and explored applications of Machine Learning on satellite data to measure CO2 emissions, estimate deforestation rates, and identify areas at risk of flooding.

The Data Science Institute also provided consistent exposure to leading faculty and data professionals from day one. That gave me the confidence I needed to take risks early on, and it played a huge role in helping me secure an excellent internship opportunity at BNP Paribas, and a rewarding research position in the Irving Medical Center.

What is your favorite movie or TV show about AI and why? 

The Social Network. Although it isn’t about AI per se, it conveys just how accidental the success of social networks was in the 2000s. Since then, they have massively disrupted how we access information and interact with each other, and we still don’t fully understand their effects on society.

Today, with the democratization of AI, we’re experiencing a similar flavor of disruption, but it’s happening at an even larger scale and over a shorter time frame. And once again, none of it feels organized. The movie did a good job of inducing a feeling of tenseness as events unfold. I know many data scientists who have had that exact same feeling when they consider how fast technology is evolving in the AI space.