Senior Data Scientist, Quantitative Research & AI

Job Summary

The Senior Data Scientist will lead quantitative investment research and develop data science and AI-enabled solutions that support investment decision-making, research efficiency, and analytical workflows. This senior individual contributor role requires strong quantitative research expertise, practical investment judgment, hands-on technical capabilities, and the ability to mentor other data scientists and researchers.

The ideal candidate has significant experience in equity factor research, signal discovery, backtesting, investment analytics, and the application of modern AI technologies, including large language models, intelligent agents, retrieval-based systems, structured information extraction, and research-support tools.

Duties & ResponsibilitiesLead research into alpha factors across market, fundamental, event-related, alternative, and text-based datasets that may contribute to investment strategies.

Translate complex investment questions into testable hypotheses, research frameworks, validation methodologies, and implementation recommendations.

Design, implement, and review feature-engineering pipelines that transform market, fundamental, and unstructured data into robust research-ready features.

Apply statistical modeling, machine learning, natural language processing, and data-mining techniques to investment research challenges.

Evaluate signal efficacy, robustness, implementation feasibility, capacity considerations, and risk characteristics.

Conduct and oversee backtesting activities, including point-in-time data handling, bias controls, transaction-cost considerations, regime analysis, and ongoing monitoring.

Interpret and apply quantitative-finance concepts including factor exposures, alpha decay, risk attribution, turnover, drawdown analysis, signal stability, and market-cycle behavior.

Build and enhance AI-enabled research workflows utilizing large language models, retrieval-based systems, intelligent agents, structured outputs, workflow orchestration, and research automation capabilities.

Develop evaluation methodologies and quality-control processes for AI-enabled applications, including benchmarking frameworks, testing approaches, error analysis, consistency evaluations, and output validation.

Prepare research reports, technical documentation, and presentations for quantitative researchers, portfolio managers, investment professionals, and technical stakeholders.

Partner closely with portfolio managers, researchers, and investment teams to translate research findings into investable insights, portfolio applications, risk-management tools, and workflow improvements.

Mentor team members through research reviews, code reviews, model validation, and practice sharing.

Identify and resolve research, data-quality, infrastructure, and workflow challenges while maintaining high standards of reliability, documentation, and analytical rigor.Leadership ExpectationsServe as a senior contributor and technical lead across quantitative research and AI-related initiatives.

Provide hands-on mentorship and technical guidance to data scientists and researchers, including research design, coding standards, validation techniques, and communication effectiveness.

Build strong collaborative relationships across investment, research, data, and technology functions.

Promote high standards of research reproducibility, documentation, review discipline, model evaluation, and practical investment relevance.

Help drive continuous improvement in research methodologies, analytical frameworks, and AI-enabled capabilities..Competenciesgood levels of integrity, confidentiality, and professional maturity.

Strong ownership mindset with the ability to independently lead complex and ambiguous projects.

Exceptional analytical and problem-solving skills with strong attention to detail.

Deep intellectual curiosity regarding financial markets, investment behavior, quantitative models, and emerging technologies.

Strong software-engineering discipline, including clean code, modular development, testing, documentation, and reproducibility.

Collaborative working style with the ability to partner effectively across investment, research, data, and technology teams.

Excellent written and verbal communication skills, capable of presenting complex quantitative and AI-related concepts to both technical and non-technical audiences.

Sound judgment regarding the practical application of advanced modeling, automation technologies, and AI solutions within investment workflows.