Senior Data Scientist

Role overview

A senior data scientist role focused on designing, building, validating, and improving statistical and machine-learning systems that power a financial research and market intelligence platform. The work involves large, complex datasets spanning market prices, company fundamentals, filings, corporate actions, macroeconomic indicators, news, research metadata, and product telemetry, with a strong emphasis on whether models are statistically sound, explainable, operationally useful, and robust enough for professional financial research. The position is remote and collaborates closely with data engineering, AI engineering, quantitative research, software, product, and regional research teams to take analytical ideas from exploratory work into reliable production workflows.

Responsibilities

- Design, develop, and evaluate statistical and machine-learning models that support research, analytics, and product workflows on a financial intelligence platform - Analyze large structured and semi-structured datasets covering market prices, company fundamentals, filings, corporate actions, macroeconomic indicators, news, and telemetry signals - Establish rigorous evaluation practices, including defensible methodology, leakage controls, robustness checks, and clear documentation of assumptions and limitations - Engineer features, design train/validation/test splits, run cross-validation, and track experiments so that findings can be reproduced and audited - Partner with data engineering, AI, software, quantitative research, product, and regional research teams to move prototypes into maintainable production systems - Translate financial-research questions into measurable analytical requirements and communicate findings, including caveats, to non-technical stakeholders

Requirements

- 5+ years of professional experience in data science, applied machine learning, quantitative analytics, or statistical modeling - Strong proficiency in Python for analysis, modeling, and research, plus experience with libraries such as pandas, NumPy, SciPy, scikit-learn, statsmodels, and matplotlib - Advanced knowledge of statistics, probability, hypothesis testing, regression, experimental design, and model evaluation - Hands-on experience with large, complex, or time-dependent datasets and strong SQL skills on relational or analytical systems - Track record of building and validating machine-learning models in production or near-production environments, with attention to reproducibility, data lineage, and documentation - Ability to spot statistical weaknesses, unsupported assumptions, and misleading interpretations, and to explain them clearly

Benefits and work setup

- Annual base salary of USD $110,000-$145,000, depending on experience, technical depth, location, and role fit - Remote work for eligible candidates based in the United States, Europe, or Hong Kong, subject to local employment, tax, and data-protection requirements - Direct collaboration across data engineering, AI, quantitative research, software, product, and regional research teams - Exposure to financial market data, quantitative research, AI-assisted workflows, and production data-science systems