Senior Data Scientist

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RemoteFull-timeSeniorData Science
Senior Data Scientist
Design, develop, validate, and improve statistical and machine-learning systems for financial research, market intelligence, and production analytics.
United States, Europe, or Hong Kong Full-time USD $110,000-$145,000/year
About Finstock, Inc.
Finstock, Inc. builds AI-powered financial research, trading analytics, quantitative research, market intelligence, and research-support infrastructure for analysts, research teams, experienced market users, and institutions.
Our products combine financial and market data, quantitative analytics, AI-assisted research workflows, source-aware analysis, and secure user-scoped systems to help professional users structure complex financial research more effectively.
About The Role
Finstock is seeking a Senior Data Scientist to design, develop, validate, and improve statistical, machine-learning, and data-driven research systems across our financial research and market intelligence platform.
In this role, you will work with large and complex datasets spanning market prices, company fundamentals, financial statements, filings, corporate actions, macroeconomic indicators, news, research metadata, product telemetry, and other approved data sources.
You will be responsible not only for developing models, but also for determining whether those models are statistically sound, appropriately validated, operationally useful, explainable, and robust enough for financial research applications.
The role will work closely with Data Engineering, AI Engineering, Quantitative Research, Software Engineering, Product, and regional research teams to move analytical ideas from exploratory research into reliable production workflows.
This is a remote position open primarily to qualified candidates based in the United States, Europe, or Hong Kong, subject to Finstock's ability to employ or otherwise engage candidates in accordance with applicable local employment, tax, data-protection, and operational requirements.
Key Responsibilities
Design, develop, and evaluate statistical and machine-learning models for financial research, market intelligence, analytics, and product workflows.
Analyze large structured and semi-structured datasets across equities, ETFs, indices, FX, commodities, crypto assets, financial statements, macroeconomic data, corporate events, and research metadata.
Conduct exploratory data analysis to identify patterns, structural relationships, anomalies, regime changes, and potential research signals.
Develop predictive, descriptive, ranking, classification, clustering, anomaly-detection, and time-series models where appropriate.
Build features and analytical datasets for downstream quantitative, AI, research, and product use cases.
Design rigorous model-validation frameworks, including out-of-sample testing, cross-validation, benchmark comparison, sensitivity analysis, and robustness checks.
Identify and mitigate common modeling risks such as overfitting, data leakage, look-ahead bias, survivorship bias, selection bias, unstable features, and regime dependence.
Evaluate models using appropriate statistical and business-relevant metrics rather than relying on a single headline metric.
Collaborate with Quantitative Researchers on factor research, market-regime analysis, signal evaluation, portfolio-risk research, and statistical market studies.
Collaborate with AI Engineers on retrieval, ranking, classification, embeddings, evaluation datasets, model-quality analysis, and AI-assisted research workflows.
Collaborate with Data Engineers to improve dataset quality, lineage, feature pipelines, schema design, reproducibility, and production reliability.
Work with Software Engineers to integrate validated models and analytical services into APIs, dashboards, internal tools, and user-facing applications.
Develop model-monitoring approaches for drift, data-quality degradation, performance deterioration, and changing market conditions.
Create clear visualizations, dashboards, technical reports, and research summaries for technical and non-technical stakeholders.
Review experiments and modeling work produced by other team members and contribute to technical standards for data science at Finstock.
Maintain reproducible notebooks, experiment records, model documentation, assumptions, limitations, and methodology notes.
Support model-risk, research-quality, and responsible-AI workflows where statistical validation is required.
Ensure all data-science work follows applicable data licensing, privacy, security, confidentiality, and internal governance requirements.
Required Qualifications
5+ years of professional experience in data science, applied machine learning, quantitative analytics, statistical modeling, or a related technical field.
Strong proficiency in Python for statistical analysis, machine learning, data processing, and research.
Advanced knowledge of statistics, probability, hypothesis testing, regression, model evaluation, and experimental design.
Strong experience with libraries such as: pandas; NumPy; SciPy; scikit-learn; statsmodels; matplotlib; or equivalent analytical tools;
Experience working with large, complex, or time-dependent datasets.
Strong SQL skills and experience working with relational or analytical data systems.
Experience developing and validating machine-learning models in production or near-production environments.
Strong understanding of train/validation/test design, feature engineering, leakage prevention, hyperparameter selection, cross-validation, and model generalization.
Ability to identify statistical weaknesses, unsupported assumptions, and misleading interpretations in analytical work.
Strong understanding of reproducibility, experiment tracking, data lineage, and model documentation.
Experience collaborating with engineering teams to move analytical prototypes into maintainable production systems.
Strong written and verbal communication skills.
Ability to explain complex statistical or machine-learning results clearly to product managers, engineers, researchers, and business stakeholders.
Ability to work independently in a distributed international environment while contributing effectively to cross-functional teams.
Professional commitment to secure and responsible handling of financial, company, and user-related data.
Preferred Qualifications
Master's degree or PhD in Statistics, Mathematics, Computer Science, Data Science, Economics, Financial Engineering, Physics, Operations Research, or a related quantitative field.
Experience in fintech, financial services, investment research, capital markets, trading analytics, market intelligence, or quantitative research.
Experience working with: time-series modeling; panel data; forecasting; anomaly detection; factor models; regime classification; ranking systems; NLP; embeddings; recommendation systems; causal inference;
Familiarity with financial concepts such as: equities and ETFs; market indices; FX; commodities; financial statements; corporate actions; volatility; risk factors; macroeconomic indicators;
Experience with gradient-boosting methods, deep learning, or other advanced machine-learning techniques where appropriate.
Experience with PyTorch, TensorFlow, XGBoost, LightGBM, or similar frameworks.
Experience with MLflow, experiment tracking, feature stores, model registries, or MLOps workflows.
Familiarity with cloud platforms such as AWS, Google Cloud Platform, or Microsoft Azure.
Experience with data warehouses, lakehouses, Spark, Databricks, Snowflake, BigQuery, or similar analytical platforms.
Experience with Docker, Kubernetes, GitHub Actions, CI/CD, and production monitoring.
Experience evaluating AI/LLM systems, retrieval systems, ranking models, or model-quality datasets.
Familiarity with responsible AI, model governance, explainability, model-risk management, or financial-model validation.
Experience mentoring junior data scientists or reviewing analytical work.
What You Will Work On
You May Work On Projects Such As
Building statistical models to identify changes in market regimes and financial-data behavior.
Developing anomaly-detection systems for market, fundamental, macroeconomic, or operational datasets.
Creating research features and derived datasets used by Findex and Finstock Research OS.
Developing ranking and relevance models for financial documents, research sources, and market context.
Evaluating AI-generated financial research outputs using statistical and model-quality frameworks.
Building models for source quality, research relevance, classification, or retrieval workflows.
Studying relationships between macroeconomic variables, market factors, sectors, and asset classes.
Developing forecasting or scenario-analysis tools for research-support workflows.
Designing model monitoring and drift-detection systems.
Building experimentation frameworks for product and research analytics.
Supporting quantitative research with statistically rigorous feature testing and validation.
Creating reusable data-science infrastructure that can support multiple Finstock product teams.
Research and Model-Quality Standards
Because Finstock operates in financial research, Senior Data Sci

Work arrangement
Yes