Senior Data Scientist, Product Analytics

Xometry is looking for a highly analytical and data-driven Senior Data Scientist (Product Analytics) with statistical skills and a passion for data to drive improvements in technology. This role will be pivotal in leveraging data to optimize product performance, drive strategic decisions, and continuously improve our user experience through rigorous testing. You will delve deeply into product data, design and analyze experiments, and provide actionable insights that directly impact our product roadmap.

Part of the scope of this role is measuring, experimenting on, and quantifying the impact of one of Xometry's highest-leverage strategic initiatives: the DFM AI + IQE integration with our partner. You will design the metric framework, experimentation strategy, and reporting that tell us whether the embedded experience inside the partner’s ecosystem - Designcenter, and Teamcenter — is actually delivering the "science fiction speed" digital thread from ideation to delivery, and how to make it better.

• →Advanced Analytics: Conduct comprehensive analysis of product usage, user behavior, and performance metrics to identify opportunities for improvement.
• →Experimentation Design & Management: Design, implement, and manage A/B tests, causal analysis, and other experiments. Define clear hypotheses, target segments, and success metrics.
• →Experimentation Analysis: Analyze experiment results using statistical methods, providing detailed reports and recommendations. Identify statistically significant findings and translate them into actionable insights.
• →Integration Measurement: Own the measurement and experimentation strategy for the embedded DFM AI + IQE integration with our partner. Define success metrics and instrumentation across the in-CAD experience (Solid Edge, NX, Designcenter) and the Xometry marketplace, design cross-surface experiments and causal analyses that join the two funnels, work through the identity-resolution, attribution, and statistical-power challenges of low-volume enterprise cohorts, and quantify the funnel from designer intent in Teamcenter to manufactured part — making the digital thread's business impact legible to leadership.
• →KPI Definition & Tracking: Define key performance indicators (KPIs) and establish robust tracking and reporting mechanisms, including KPIs that make sense in a PLM/CAD context (e.g., time-from-design-to-quote, in-CAD-to-order conversion, designer adoption per seat) as well as in the marketplace.
• →Dashboarding & Visualization: Develop and maintain dashboards to visualize experiment results, key metrics, and trends, making data accessible and understandable to stakeholders.
• →Collaboration: Work closely with product managers, engineers, designers, machine learning scientists, and — for the Siemens integration — partner engineering teams to implement and iterate on experiment findings and product improvements.
• →Data Storytelling: Communicate complex data insights and experiment results clearly and compellingly to diverse audiences, including leadership.
• →Data Integrity: Ensure data accuracy and integrity for all analysis and experimentation, including the cross-system data flows that span the Siemens and Xometry surfaces.
• →Methodology Development: Contribute to the development and improvement of our experimentation methodologies and best practices, especially for partner-integrated and multi-surface products.

Qualifications

• →Bachelor's degree in Computer Science, Computer Engineering, Data Analytics, Mathematics, Statistics, Information Systems, Economics, or another quantitative discipline.
• →6–8 years of relevant experience.
• →Strong proficiency in data analysis, statistical modeling, and data visualization.
• →Proven experience in designing, implementing, and analyzing A/B tests and other experiments.
• →Solid understanding of statistical significance and hypothesis testing.
• →SQL is a must. Python is nice to have. Any experience with Looker is also desirable.
• →Knowledge of data science concepts and methodologies.
• →Excellent communication and presentation skills, with the ability to explain complex data insights — including to external partner engineering teams.
• →Strong analytical and problem-solving skills.
• →Experience working in a product-driven environment.
• →Attention to detail and a commitment to data accuracy.

Nice to have 

• →multi-surface / partner-integration analytics: Experience designing experiments and metrics across multi-surface or partner-integrated products (embedded experiences, CAD/PLM integrations, B2B SaaS plug-ins, extension or in-app funnels), including the identity-resolution, attribution, and statistical-power problems that come with low-volume enterprise cohorts.
• →CAD/PLM/manufacturing fluency: Familiarity with CAD, PLM, or manufacturing workflows; comfort talking with designers, manufacturing engineers, and partner engineering teams about how they actually use tools day-to-day.
• →causal methods at scale: Hands-on experience with quasi-experimental and causal inference methods (difference-in-differences, synthetic control, CUPED, switchback, geo-experiments) for situations where clean A/B isn't feasible — common when the experimental unit is an enterprise account or a designer inside a partner environment.