Senior Data Scientist | Remote

Senior Data Scientists

We are seeking experienced data scientists to evaluate real-world data science work for an advanced AI research project.

You will define what high-quality data science looks like, create clear scoring criteria, and evaluate completed analyses with detailed written reasoning.

What You'll Do

• Develop grading criteria for data analyses, predictive models, dashboards, experiments, metric frameworks, and business recommendations
• Evaluate AI-generated and human-created data science work
• Review SQL queries, Python analyses, statistical methods, visualizations, assumptions, and conclusions
• Score work for technical accuracy, analytical rigor, business relevance, clarity, and actionability
• Assess experiment designs, A/B tests, causal claims, metric definitions, and executive recommendations
• Provide detailed, evidence-based written justifications for every score
• Apply consistent evaluation standards across different tasks
• Incorporate reviewer feedback and improve your work quickly

Who Can Apply

Relevant backgrounds include:

Data Scientists, Senior Data Scientists, Staff Data Scientists, Principal Data Scientists, Lead Data Scientists, Product Data Scientists, Growth Data Scientists, Business Data Scientists, Decision Scientists, Analytics Scientists, Applied Data Scientists, Experimentation Scientists, Causal Inference Scientists, Statistical Scientists, Quantitative Researchers, Research Scientists, Applied Scientists, and Machine Learning Scientists.

Leadership backgrounds may include:

Data Science Managers, Heads of Data Science, Directors of Data Science, Analytics Directors, Product Analytics Leads, Experimentation Leads, Insights Directors, Decision Science Managers, Quantitative Analytics Leads, and Data Strategy Consultants.

Relevant Areas of Expertise

• Product, growth, marketing, operations, marketplace, or business data science
• A/B testing, controlled experiments, holdout tests, and experimentation platforms
• Experiment design, statistical power, sample sizing, and significance testing
• Causal inference, quasi-experimental methods, and observational analysis
• Metric definition, KPI development, north-star metrics, and guardrail metrics
• Funnel analysis, conversion analysis, retention, engagement, and cohort analysis
• Customer segmentation, churn modeling, forecasting, and predictive analytics
• Regression, classification, clustering, time-series analysis, and statistical modeling
• Dashboard development, data visualization, and executive reporting
• Translating technical findings into clear business recommendations
• Reviewing analytical work for errors, bias, leakage, weak assumptions, or unsupported conclusions

Tools and Technologies

Experience with one or more of the following is valuable:

Python, SQL, R, pandas, NumPy, SciPy, scikit-learn, statsmodels, Jupyter, Spark, Databricks, Snowflake, BigQuery, Redshift, Tableau, Power BI, Looker, Mode, Amplitude, Mixpanel, Excel, dbt, Git, Airflow, and modern experimentation platforms.

Requirements

• 5+ years of professional data science experience in an industry environment
• Strong hands-on experience with SQL and Python or R
• Deep understanding of statistics, experiment design, A/B testing, and analytical methodology
• Experience defining metrics and evaluating business or product performance
• Ability to assess whether an analysis, model, or recommendation is technically sound
• Strong written communication and the ability to explain professional judgment clearly
• Detail-oriented, consistent, and comfortable receiving structured reviewer feedback

Preferred Background

• Experience in product, growth, business operations, marketplace, consumer, or technology data science
• Experience presenting findings to executives, product leaders, or business stakeholders
• Experience reviewing the work of other data scientists or analysts
• Background at a technology company, consulting firm, research organization, or data-driven consumer business
• Prior experience with AI evaluation, model training, data annotation, rubric development, benchmark creation, or human-feedback projects

This opportunity is ideal for experienced data professionals who can distinguish rigorous, decision-ready data science from technically plausible but weak or misleading analysis. We are a referral partner of the client