Leadership & Team Development

• Provide technical and methodological leadership to the Data Science team, remaining hands-on where needed while guiding the team through complex analytical and modeling challenges.
• Establish best practices for data science work in a regulated banking environment, including documentation, model explainability, validation, and reproducibility.
• Foster responsible experimentation with GenAI and advanced analytics in approved environments, ensuring learnings are shared and aligned with Governance and Compliance.
• Monitor developments in AI and Data Science (e.g. GenAI patterns, agent-based workflows, model optimization techniques) and assess their real applicability and risk within banking constraints.

End-to-End Project Delivery

• Own the delivery lifecycle from business problem definition through data exploration, feature engineering, modeling, validation, and handover to production teams.
• Work closely with Analytics, BI, Data Warehousing, Big Data, and ML Engineering to ensure data readiness, model feasibility, and scalable implementation.
• Ensure experimental models and notebooks are production-ready through collaboration with ML Engineering, respecting operational, security, and performance requirements.
• Promote the use of interpretable and auditable models for customer-facing, risk-related, or regulatory-sensitive use cases.

Business Partnership & Communication

• Act as a trusted partner for business stakeholders, helping translate business needs (e.g. personalization, segmentation, forecasting) into clear analytical objectives.
• Communicate results and insights in a business-oriented, non-technical manner, with a focus on impact, limitations, and decision support.
• Set realistic expectations around what Data Science and GenAI can and cannot deliver, clearly articulating risks, assumptions, and timelines.
• Support alignment with Data Governance, Model Risk Management, and Compliance frameworks.

Qualifications & Skills

Core Technical Skills

• 5+ years of experience in Data Science, with proven delivery of models used in real business scenarios.
• Strong foundation in classical and explainable machine learning techniques (regression, classification, clustering, feature selection, model evaluation).
• Solid hands-on skills in Python and SQL, with a good understanding of enterprise data landscapes (DWH, data lakes).
• Experience working with cloud environments (Azure or AWS) for data science and ML workloads, including experiment tracking (e.g. MLflow).
• Practical exposure to GenAI / LLM use cases, with a clear understanding of governance, data privacy, and model limitations in regulated environments.

Approach & Background

• Analytical mindset with the ability to view problems end-to-end, from data availability and quality to business consumption and value.
• Comfortable operating at the intersection of data, technology, and business, balancing innovation with control.
• Degree in quantitative field (Computer Science, Statistics, Mathematics, Engineering, or similar).

Nice to Have

• Experience with Snowflake and/or Databricks within enterprise or regulated environments.
• Familiarity with Big Data frameworks (e.g. Spark).
• Experience operationalizing data science outputs as monitored, governed products, not just experiments.
• Exposure to GenAI development frameworks, rapid prototyping tools or AI-assisted development workflows (e.g., Claude Code, Cursor, GitHub Copilot, OpenAI Codex), applied responsibly.
• Personal projects or portfolio demonstrating applied Data Science / ML work (optional).

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