Xometry is looking for a Senior Manager, Data Science & Operations Analytics to join our team. You will lead the analytics strategy that enables data-driven decision making across Xometry's AI-powered manufacturing marketplace. Your team will generate insights, develop statistical frameworks, build scalable analytical data assets, and establish experimentation capabilities that improve marketplace performance, operational efficiency, customer experience, and profitability. Working closely with Product, Operations, Sourcing, Pricing, Engineering, and Finance, you will help answer the company's most important business questions through rigorous analysis and scientific experimentation. Your team will focus on identifying opportunities, quantifying impact, measuring outcomes, and providing the analytical foundation that informs product development and operational strategy. This role is ideal for a leader who combines deep analytical expertise with business acumen and enjoys solving complex marketplace problems using statistics, experimentation, and data. Your work will directly influence product roadmaps, quarterly business priorities, go-to-market initiatives, and company-wide decision making, to support our mission to become the "Everything Store" for custom manufacturing. Working at the intersection of technology, manufacturing, and marketplace dynamics, you'll help shape Xometry's future while delivering exceptional value to both buyers and suppliers.

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.
• Lead forecasting and trend analysis for marketplace demand, supplier capacity, sourcing operations, fulfillment performance, and financial outcomes.
• 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.

The estimated base salary range for new hires into this role is $170,000 - $185,000 annually + annual bonus depending on factors such as job-related skills, relevant experience, and location. We also offer a competitive benefits package, including 401(k) match, medical, dental and vision insurance; life and disability insurance; generous paid time off including vacation, sick leave, floating and fixed holidays, maternity and bonding leave; EAP, other wellbeing resources; and much more.

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