Senior Data Scientist

Back to careers

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 Scientists are expected to apply a high standard of analytical discipline.

You Should Be Comfortable Identifying And Addressing

• Look-ahead bias
• Survivorship bias
• Data leakage
• Overfitting
• Multiple-testing risk
• Selection bias
• Regime instability
• Non-stationarity
• Outliers and structural breaks
• Missing or stale data
• Weak benchmark selection
• Misleading correlations
• Unstable feature importance
• Inappropriate evaluation metrics
• Insufficient sample sizes
• Unrealistic assumptions
  
  

Models should be documented with their intended use, assumptions, limitations, validation methodology, and known risks.

Collaboration

You Will Work Closely With

Data Engineering: To develop reliable datasets, feature pipelines, data-quality controls, lineage, and scalable analytical infrastructure.

Quantitative Research: To evaluate statistical hypotheses, financial signals, market regimes, risk factors, and quantitative research methodologies.

AI Engineering: To support model evaluation, retrieval, ranking, embeddings, research-quality systems, and AI-assisted financial workflows.

Software Engineering: To integrate analytical models into production APIs, dashboards, internal systems, and user-facing features.

Product: To translate financial-research problems into measurable analytical and product requirements.

Regional Research Teams: To incorporate financial-domain expertise and market-specific context into data-science workflows.

What We Offer

• 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.
• Direct exposure to financial market data, quantitative research, AI systems, market intelligence, and production data-science workflows.
• Opportunity to influence Finstock's statistical modeling, machine-learning, and research-quality standards.
• Direct collaboration with engineering, AI, data, quantitative research, product, and market-research teams.
• Access to approved company development, research, collaboration, and productivity tools required for the role.
• Opportunity to participate in approved company meetings or offsite activities where relevant, subject to business requirements, travel eligibility, and company policy.
  
  

Important Role Boundaries

This is a data-science role supporting financial research, analytics, model evaluation, and product-development workflows.

The role does not require or permit the employee to:

• Provide personalized investment, legal, tax, accounting, or financial advice to users or clients.
• Recommend that an individual buy, sell, or hold a financial instrument based on personal circumstances.
• Execute trades or manage client funds.
• Handle client assets, deposits, withdrawals, or payments.
• Present model outputs, forecasts, backtests, or statistical relationships as guaranteed future outcomes.
• Misrepresent correlation as causation without appropriate evidence.
• Use unauthorized datasets or violate third-party licensing restrictions.
• Bypass internal privacy, security, access-control, or model-governance requirements.
• Deploy models into production without required validation or approval.
• Request applicants to pay application fees, recruitment fees, training payments, software payments, equipment payments, or onboarding fees.
  
  

Location and Work Eligibility

This Role Is Intended For Qualified Candidates Based In

• United States
• Europe
• Hong Kong
  
  

Candidates must be legally eligible to work under the employment or engagement structure applicable to their location.

Finstock may not be able to employ candidates in every jurisdiction. Final employment terms, employing entity, payroll arrangements, statutory benefits, and local requirements will depend on the candidate's location and the applicable written agreement.

Ideal Candidate

The ideal candidate combines strong statistical foundations with practical engineering judgment and financial-domain curiosity.

You should be comfortable moving between exploratory analysis, rigorous model validation, production implementation, and communication with non-data-science stakeholders.

You should care not only about whether a model performs well, but also:

• whether the underlying data is reliable;
• whether the model is robust;
• whether the evaluation methodology is defensible;
• whether the result is interpretable;
• whether the model remains useful under changing market conditions;
• whether its limitations are clearly communicated.
  
  

Equal Opportunity Statement

Finstock, Inc. considers qualified applicants based on role-related skills, experience, technical ability, analytical quality, work quality, availability, and applicable employment requirements.

We do not make hiring decisions based on age, gender, gender identity, religion, ethnicity, race, national origin, disability, sexual orientation, marital status, family status, veteran status, or any other status protected by applicable law.

Finstock, Inc. does not request application fees, recruitment fees, training payments, software payments, equipment payments, or onboarding fees from candidates.

Role Application

Apply for this role

Submit your information for the Senior Data Scientist role.

Open

Full name *

Email address *

Country / region *

Current location / time zone *

Phone number *

LinkedIn profile URL *

Resume/CV link or portfolio link *

Describe your work authorization in your country of residence. *

Years of professional data science, machine learning, or statistical modeling experience *Select experience rangeLess than 3 years3-5 years5-8 years8+ years

Current or most recent role *

Expected annual compensation *

Availability / earliest start date *

Describe a statistical or machine-learning model you developed, its intended use, and how it reached production. *

How did you validate a model, prevent data leakage or look-ahead bias, and test robustness against an appropriate benchmark? *

Describe your Python, SQL, statistical analysis, and machine-learning tools and how you used them. *

Have you worked with market data, company fundamentals, filings, corporate actions, macroeconomic indicators or financial datasets? Explain briefly. *

Describe your approach to reproducibility, model documentation, drift monitoring, and communicating limitations. *

Additional notes

I confirm that the information I provide is accurate and that I understand this role does not permit personalized investment advice, trade execution, management of client funds, guaranteed model outcomes, unauthorized data use, or deployment without required validation and approval.I acknowledge that I have read the Applicant Privacy Notice.I would like Finstock, Inc. to retain my application for consideration for future roles where permitted by applicable law.

Role summary

Company Finstock, Inc.

Location Remote - United States, Europe, or Hong Kong

Employment Full-time

Base salary USD $110,000-$145,000/year, depending on experience, technical depth, location, and role fit

Reporting to Head of Data / Engineering Leadership

Collaboration Data Engineering, AI Engineering, Quantitative Research, Product, Software Engineering, and Research teams

Apply now