Senior Data Scientist | Remote

Job Description 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 workReview SQL queries, Python analyses, statistical methods, visualizations, assumptions, and conclusionsScore work for technical accuracy, analytical rigor, business relevance, clarity, and actionabilityAssess experiment designs, A/B tests, causal claims, metric definitions, and executive recommendationsProvide detailed, evidence-based written justifications for every scoreApply consistent evaluation standards across different tasksIncorporate 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 platformsExperiment design, statistical power, sample sizing, and significance testingCausal inference, quasi-experimental methods, and observational analysisMetric definition, KPI development, north-star metrics, and guardrail metricsFunnel analysis, conversion analysis, retention, engagement, and cohort analysisCustomer segmentation, churn modeling, forecasting, and predictive analyticsRegression, classification, clustering, time-series analysis, and statistical modelingDashboard development, data visualization, and executive reportingTranslating technical findings into clear business recommendationsReviewing 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 RDeep understanding of statistics, experiment design, A/B testing, and analytical methodologyExperience defining metrics and evaluating business or product performanceAbility to assess whether an analysis, model, or recommendation is technically soundStrong written communication and the ability to explain professional judgment clearlyDetail-oriented, consistent, and comfortable receiving structured reviewer feedback Preferred Background - Experience in product, growth, business operations, marketplace, consumer, or technology data scienceExperience presenting findings to executives, product leaders, or business stakeholdersExperience reviewing the work of other data scientists or analystsBackground at a technology company, consulting firm, research organization, or data-driven consumer businessPrior 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 .