Responsibilities
- Research, design and build advanced NLP & data science solutions to address ESG-related challenges (e.g., company carbon emissions estimations, ESG data extraction from company filings).
- Collaborate with engineering and operations teams to deploy robust and scalable models, pipelines, and user interfaces.
- Maintain and enhance existing methodologies and codebases to add new features and improve quality. Stay on top of the latest R&D in data science, NLP (GenAI), competitor offerings, and sustainable finance.
- Advise internal and external partners on knowledge of SFI models as a Subject Matter Expert (SME).
- Data Analysis and Integration Identify and assess data sources, as well as develop data queries needed by projects.
- Analyze data and identify patterns to ensure practical, customer focused and production-ready designs. Integrate internal and external data sources into end-to-end pipelines for solution development.
- Project Management & Collaborator Engagement Collaborate with cross-functional team members across research, engineering and operations to drive successful project outcomes.
- Provide technical leadership in project planning, deliverable scoping and solution architecture.
- Proactively identify potential risks and issues, and engaging collaborators to develop and implement mitigation strategies.
- Effectively communicate complex analyses and insights to both technical and non-technical audiences through clear reports, visualizations and presentations.
Basic qualifications
- Bachelor, Master or Ph.D. degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
- Minimum 5 years’ working experience as a Data Scientist or similar roles, with emphasis on machine learning, NLP or advanced analytics.
- Strong coding skills with track record of delivering data science solutions in commercial products or platforms.
- Advanced Python: data analysis (pandas/polars), data query (SQL, APIs), modular programming, performance optimization, etc.
- Core Expertise: statistics, machine learning, model evaluation, and classical NLP techniques.
- Ability to produce clear, structured technical and non-technical materials.
- Collaboration Tools: Git, containerization (Docker) and cloud platforms (AWS, Azure) LLM Fundamentals: understanding of use cases, cost structures, human-in-the-loop workflows, and RAG.
Preferred qualifications
- Experience in developing LLM-based solutions for commercial products or platforms.
- Experience with building interactive user interfaces (e.g., Gradio, Streamlit, Dash)
- Experience with model monitoring tools (e.g., MLflow, Weights & Biases)
- Experience with agentic AI
- Research experience in machine learning, NLP or ESG
- Work experience in financial industry or ESG-related projects.
Tags & focus areas
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