Senior Data Scientist

The Senior Data Scientist will sit within the product organization and help drive data-informed product decisions across reporting, deep-dive analysis, product evaluations, and AI data initiatives. This role is best suited for someone who combines strong analytical rigor with product judgment and can turn ambiguous business questions into clear metrics, structured analysis, and actionable recommendations.  This person will partner closely with Product, Engineering, Analytics, and business stakeholders to define success metrics, evaluate product performance, identify opportunities, and communicate insights that influence roadmap and investment decisions. The role will balance hands-on analysis with strategic thought partnership, with an emphasis on product health, KPI development, feature evaluation, and scalable analytics practices.  Key Responsibilities  Partner with Product Managers, Engineers, and cross-functional stakeholders to answer strategic product questions through reporting, analysis, deep dives, and predictive or prescriptive modeling.  Forecast business outcomes and identify opportunities to improve product performance.  Define, maintain, and improve key product metrics, dashboards, and monitoring frameworks used to assess product performance and business impact.  Apply machine learning techniques to customer behavior, engagement, retention, churn, propensity scoring, segmentation, recommendation systems, and personalization initiatives  Evaluate product launches, feature changes, and AI-enabled initiatives by measuring outcomes against success metrics and communicating clear recommendations.  Support experimentation and measurement design, including hypothesis development, test readouts, and post-launch evaluations where applicable.  Build scalable modeling, reporting, and self-service analytics assets that improve decision-making speed and consistency across the product organization.  Partner with data engineering and platform teams to improve data quality, instrumentation, metric definitions, and the reliability of reporting pipelines.  Translate complex findings into concise narratives, presentations, and recommendations for technical and non-technical stakeholders.  Support AI data initiatives by helping build, evaluate models, measure features, and build analytics frameworks that assess customer and business impact.  Qualifications  Bachelor's degree in a quantitative field such as Statistics, Economics, Mathematics, Computer Science, Engineering, or a related discipline; advanced degree preferred.  5+ years of experience in data science, product analytics, decision science, or a closely related analytics role supporting product teams.  Strong SQL skills and proficiency in Python for data analysis, statistical modeling, predictive modeling, and data exploration.  Experience with PyTorch, scikit-learn, and other machine learning frameworks for building, evaluating, and deploying models. Familiarity with feature engineering, model selection, cross-validation, hyperparameter tuning, and performance evaluation.  Experience developing dashboards and visualizations that support executive reporting and day-to-day product decisions.  Strong understanding of product metrics, experimentation, hypothesis testing, and statistical analysis.  Experience working with large, messy, and multi-source datasets to produce actionable recommendations.  Excellent communication skills, with the ability to explain technical findings in plain language and influence stakeholders through data.  Experience supporting AI or ML-related product initiatives, model evaluations, or measurement frameworks is preferred.  Preferred Experience  Experience in media, ad tech, mar tech, digital subscriptions, marketplaces, or other product-led businesses.  Experience in cloud data environments and product instrumentation best practices.  Experience working in highly cross-functional environments where priorities shift quickly, and decision support is critical.  #LI-REMOTE #LI-NR2 The annualized base salary for this role will range between $115,000 and $120,000.  Base compensation is reflective of many factors, including, but not limited to, the market in which one lives/works, individual education level, skills, certifications, and experience. Note: variable compensation is not reflected in these figures and, based on the role, may be applicable.