Senior Manager, Data Science & Operation Analytics

Responsibilities

• Lead strategic analytics across marketplace performance, customer behavior, sourcing operations, pricing, and business trends.
• Develop analytical frameworks and AI agents that measure marketplace health across customer acquisition, conversion, supplier engagement, operational efficiency, pricing competitiveness, and margin performance.
• Translate complex business problems into structured analytical approaches that drive actionable recommendations.
• Deliver executive-ready insights that influence product roadmap, operational priorities, and investment decisions.
• Establish best practices for statistical analysis, hypothesis testing, causal inference, and experimental design across Product and Operations initiatives.
• Partner with Product and Engineering teams to design, measure, and interpret A/B tests and operational experiments.
• Apply advanced statistical techniques to identify drivers of customer behavior, supplier performance, operational bottlenecks, and marketplace dynamics.
• Develop measurement methodologies that quantify the business impact of new features, pricing strategies, sourcing initiatives, and operational improvements.
• Promote analytical rigor by ensuring robust methodologies, reproducible analyses, and appropriate statistical confidence in decision making.
• Define and build reusable analytical datasets, KPI frameworks, semantic layers, and metrics that enable consistent reporting and self-service analytics.
• Partner with Data Engineering to improve data quality, governance, instrumentation, and accessibility across critical business domains.
• Develop scalable dashboards and reporting solutions that provide visibility into marketplace performance and operational health.
• Standardize business definitions and measurement methodologies across teams to ensure alignment on key performance indicators.
• Analyze operational performance to identify root causes, emerging risks, and opportunities for process improvement.
• Support capacity planning through statistical forecasting and scenario analysis.
• Build analytical tools that enable proactive decision making across Operations, Pricing, and Sourcing teams.
• Lead, mentor, and develop a high-performing team of data scientists and analytics professionals.
• Foster a culture of analytical excellence, intellectual curiosity, and evidence-based decision making.
• Establish best practices for analytics development, code quality, documentation, peer review, and knowledge sharing.

Qualifications

• 7–10 years of experience in Data Science, Analytics, Statistics, Economics, Operations Research, or a related quantitative field.
• Strong foundation in statistical inference, experimental design, causal analysis, forecasting, and quantitative modeling.
• Demonstrated experience designing and analyzing A/B tests, observational studies, and business experiments.
• Expertise in SQL and Python (Pandas, NumPy, SciPy, Statsmodels) for data analysis and statistical computing.
• Experience building analytical datasets, KPI frameworks, semantic models, and scalable reporting solutions.
• Strong understanding of business intelligence, data visualization, and storytelling using tools such as Tableau, Looker, or Power BI.
• Experience working with large-scale transactional datasets in marketplace, technology, manufacturing, supply chain, or e-commerce environments.
• Ability to communicate complex statistical concepts to technical and non-technical stakeholders and influence decisions through data.
• Experience managing and developing analytics or data science teams in a cross-functional environment.
• Master's degree in Statistics, Economics, Operations Research, Data Science, Computer Science, Engineering, Mathematics, or a related quantitative discipline preferred.
• MBA or consulting experience is a plus.