Senior Data Science Manager – Data, Modeling & Analytics

Overview

Data Science Manager Senior role focuses on delivering decision ready insight through descriptive ("what happened?") and diagnostic ("why did it happen?") analytics. Concrete outputs include executive dashboards, KPI frameworks, trend and cohort analyses, drill downs, segmentation, driver analysis, and root‑cause investigations. The role is accountable for translating complex data into clear performance narratives by defining standardized metrics, ensuring data quality and lineage, and embedding analyses into scalable BI assets rather than ad hoc reporting.

It performs prescriptive analytics by turning findings into decision recommendations through scenario analysis, sensitivity testing, and practical optimization or decision models, operationalizing insights into dashboards, alerts and playbooks. Predictive analytics ("what will happen?") is in scope but not required; forecasting, risk or propensity scoring, and applied machine learning are nice‑to‑have. The manager leads and prioritizes an analytics roadmap, coaches analysts and data scientists, and promotes disciplined use of tools such as SQL, Python, modern BI platforms, cloud data technologies, Git, and applied statistics.

Strong partnership with product, operations, risk, and business leaders is essential to frame the right questions and deliver executive‑ready storytelling that connects analysis to action. The role enforces data governance, documentation, and audit‑ready practices, ensuring metric consistency and trusted reporting across teams. An experiment and measurement mindset is expected, applying test‑and‑learn or A/B testing to evaluate impact. Models or advanced techniques used are monitored for performance, reliability, and fairness, with assumptions and limitations communicated transparently.

• Proven experience delivering descriptive and diagnostic analytics, including KPI frameworks, executive dashboards, trend/​cohort analysis, segmentation, driver analysis, and root‑cause investigations that inform business decisions.
• Strong capability to translate analytical findings into prescriptive analytics, such as clear decision recommendations, scenario and sensitivity analysis, and practical optimization or decision models embedded into dashboards, alerts, or playbooks.
• Demonstrated people and delivery leadership, including owning and prioritizing an analytics roadmap, coaching analysts and data scientists, setting analytical standards, and ensuring consistent, high‑impact outcomes.
• Hands‑on proficiency with SQL, Python, BI visualization tools such as Tableau and Plotly, cloud data platforms, Git, and applied statistics, along with a strong foundation in data quality, metric definition, governance, documentation, and experimental or test‑and‑learn approaches.