Data Scientist
Key Responsibilities
End-to-End Model Ownership
Own the full lifecycle of machine learning and analytics solutions, from sourcing and validating financial and alternative datasets through to building, deploying, and maintaining production-grade models that support investment, risk, or operational decisions.
Analytical Rigor & Validation
Design and execute robust analytical experiments with clearly defined success criteria, ensuring models are statistically sound, explainable, and deliver measurable value to investment performance, risk management, or operational efficiency.
Stakeholder Partnership & Communication
Act as a trusted partner to investment professionals, risk, and operations teams by translating complex technical insights into clear, actionable outputs that align with regulatory constraints and commercial objectives.
Operational Excellence & Scalability
Enhance existing data and modelling platforms through disciplined feature engineering, performance monitoring, and close collaboration with engineering teams to ensure solutions are resilient, auditable, and scalable within an enterprise environment.
Strategic Insight & Opportunity Identification
Proactively identify opportunities where data science and automation can improve alpha generation, portfolio construction, client insights, or process efficiency, aligning initiatives with long-term firm strategy.
Team & Industry Contribution
Maintain high standards for model development, documentation, and governance, contribute to knowledge sharing and mentorship, and selectively adopt modern analytics and AI techniques where they add real investment or operational value.
Requirements
- Demonstrated experience applying data science or machine learning to real-world problems, ideally within financial services, investment management, or other regulated environments.
- Strong Python capability, with hands-on use of analytical and ML libraries (e.g. pandas, NumPy, scikit-learn or equivalent).
- Solid SQL skills and experience working with large, structured financial or transactional datasets.
- Sound understanding of core machine learning concepts, including model development, validation, feature engineering, and performance monitoring.
- Exposure to generative AI or LLM-based applications, such as prompt design, evaluation of model outputs, or integration of third-party APIs into analytical workflows.
- Familiarity with software engineering best practices, including version control (Git), testing, and writing reproducible, well-documented code.
- Ability to structure ambiguous investment or business questions into rigorous, data-driven analyses with clear metrics and outcomes.
- Strong collaborative mindset, comfortable working across investment, risk, technology, and business teams.
- Clear and confident communication skills, with the ability to explain technical trade-offs and limitations to non-technical stakeholders.
FAQs
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