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Jobboost Climate Analytics and Alignment (C2A) – Data Scientist

BNP Paribas • France • Added on Oct 30, 2026
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This Data Scientist will build the analytical methods and tools BNP Paribas uses to assess whether lending portfolios align with its climate objectives. The work sits between data science, sector expertise and banking strategy, with a focus on turning climate-alignment analysis into repeatable processes for high-emitting industries.

The central question is how the bank’s credit exposure relates to a pathway toward net zero by 2050. By improving sector-specific methods and portfolio simulations, the role can help teams assess transition progress across activities such as energy, transport, steel and real estate, and inform the bank’s engagement with clients. Its contribution is analytical, supporting decisions rather than setting client policy alone.

This suits someone with a Master’s degree, at least three years in data science or analytics, strong Python skills, and an interest in applying quantitative methods to climate transition challenges in a large international bank.

Job FAQs

What are the main missions and responsibilities of this role?

The role focuses on measuring how BNP Paribas’s credit portfolio aligns with the Group’s climate objectives. This includes preparing sector analyses, refining methodologies, documenting them and using simulations to examine portfolio impacts across emissions-intensive industries. A second strand is turning the analysis into repeatable tools and processes, including adapting programs, addressing missing data and supporting an internal dashboard and the GoNetZero project. The longer-term aim is to provide useful evidence for sector strategy and client transition discussions.

Key learning opportunities for this job

The role could build expertise in climate-alignment measurement, sector transition pathways and sustainable finance within a major banking group. Working across areas such as energy, automotive, property, steel and transport may also deepen understanding of how climate targets are translated into portfolio indicators. The position offers scope to develop Python-based analytics, data modelling, simulation and dashboard design, alongside experience improving analytical methods for use at scale. Collaboration with data scientists and sector specialists could broaden both technical and financial-sector knowledge; specific training or certification is not stated.

How does the ideal candidate look like (experience, skills)?

The stated profile is a Master’s degree in engineering, a university discipline or an equivalent qualification, with at least three years of experience in data science or data analytics. Proven Python programming skills are required, while prior climate-related experience is an advantage. The work also calls for analytical and synthesis skills, initiative, clear written and verbal communication, and comfort collaborating across specialist teams. Fluency in both French and English is required.

Advice to stand out and make a successful application

Use the application to show how your Python and analytics experience can support climate-alignment decisions, rather than describing technical tools in isolation. Give a concise example of a data project involving incomplete information, sector indicators or portfolio-level analysis, and explain the method, assumptions and outcome. If you have climate experience, connect it to measurable emissions or transition pathways. If not, show how you have learned a complex domain and worked with subject-matter experts. Prepare to discuss how you would document a methodology and communicate its limits to both data specialists and banking colleagues.

What aspects of the company's sustainability is this role likely to focus on?

The role is directly tied to the bank’s aim of steering financing toward a net-zero trajectory by 2050. Its likely focus is measuring credit-portfolio alignment with climate objectives, especially for sectors identified in the posting as high emitters. The work could support the bank’s assessment of transition pathways and its engagement with corporate clients, while providing analysis for sector strategies. The description centers on climate and financed emissions; it does not specify broader environmental or social targets for this position.

What are the main challenges someone in this role might face?

A likely challenge is producing comparable sector assessments when methods, indicators and available data differ across industries. The posting specifically mentions modelling missing data, which means analysts may need to make assumptions explicit and communicate uncertainty clearly. There is also a balance between methodological rigor and operational use: analyses must be sound while tools and review processes work across multiple sectors. Coordinating data scientists, sector experts and banking teams may require translating technical results into evidence that supports practical decisions.

How could a typical day look like for someone in this position?

A typical day could combine Python-based analysis with discussion of sector assumptions and climate indicators. The person may review portfolio data, run or refine simulations, investigate gaps, and document a methodology or its updates. Time could also go toward improving the internal dashboard or supporting the industrialization of GoNetZero. Priorities may shift as sector specialists request analysis or as the team develops its approach for a particular industry.

What are the opportunities for professional growth and development in this role?

The position could develop a combination of data science, banking and climate-transition expertise. Exposure to several emissions-intensive sectors may help the person build a broader understanding of financed emissions and the methods used to assess portfolio alignment. The role also offers experience working across Group and Corporate & Institutional Banking teams, with potential to take on more complex analytical work or contribute to wider climate-strategy projects over time. The posting describes skill and network development, but does not specify a formal promotion path.

The main stakeholders you might be interacting with

The closest collaborators are the C2A Data Analytics team, including data scientists and sector experts. The role is also expected to work with the Global Banking front office and the Low Carbon Transition Group. These interactions could involve validating sector assumptions, interpreting portfolio simulations, refining indicators and sharing data findings for client or sector strategy discussions. Project partners involved in the internal dashboard and GoNetZero work may also be part of the day-to-day network.

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