• What should change?
• For which segments?
• What is the expected impact?
• What are the risks and trade-offs?
• How should success be measured?

Key Responsibilities Proactive Strategy Analytics

• Identify patterns, inefficiencies, and improvement opportunities in sourcing and placement decisions.
• Surface insights that inform strategic and operational changes.

Root-Cause & Driver Analysis

• Decompose key outcomes such as:

• Why orders require manual placement instead of automated flows

• What drives lead time misses

• What causes margin gaps

• Analyze drivers by segment, technology, partner profile, activity level, and process step.

Segmentation & Rules Design

• Propose meaningful segmentations (e.g. order value buckets, technology subcategories, complexity tiers, partner activity levels).
• Translate analytical insights into practical policy or rule-based recommendations.

Impact & What-If Modeling

• Build flexible calculation frameworks to estimate the impact of changes such as:

• Removing blockers

• Adjusting lead times

• Updating margin or pricing guardrails

• Quantify effects on margin, throughput, lead time, and operational KPIs.

BI & Decision Support

• Build and maintain dashboards where they materially improve decision-making.
• Ensure metrics are well-defined, trusted, and aligned across teams.

Statistical Evaluation

• Conduct statistical analyses to evaluate the effectiveness of new features, policies, or process changes.
• Use before/after comparisons, cohorting, and factor analysis to assess outcomes.

Capacity & Staffing Planning

• Analyze workload, throughput, and efficiency versus demand.

• Build models for:

• Daily and weekly workload distribution

• Placement capacity per person

• Headcount needs under different demand scenarios

Measurement & Governance

• Define success metrics and monitoring checks.
• Evaluate results of changes using structured measurement approaches and controlled rollouts where applicable.

Stakeholder Collaboration

• Work closely with Partner Management, Product, and Operations.
• Align on definitions, assumptions, insights, and next actions.
• Communicate recommendations clearly to both technical and non-technical stakeholders.

Required Qualifications

• 3+ years of hands-on experience in Data Analytics, Operations Analytics, or Business Analytics (preferably in operations, marketplaces, supply chain, sourcing, procurement, or similar domains)
• Strong SQL skills and confidence working with multiple datasets and complex joins
• Proficiency in Python, with experience using at least one core data library (e.g. pandas, SciPy, scikit-learn)
• Solid understanding of statistics and driver/factor analysis
• Good understanding of relational database principles
• Proven ability to turn analysis into clear, actionable recommendations

Preferred Qualifications

• Strong stakeholder communication skills; ability to explain complex findings clearly to non-technical audiences
• Experience with pricing, margin, or revenue analytics
• Familiarity with experimentation and evaluation methods (A/B testing, holdouts, quasi-experiments)
• Experience building BI dashboards in Looker, Retool, or similar tools
• Knowledge of supply chain, sourcing, or procurement processes and operational KPIs (lead time, on-time delivery, quality signals)

Personal Qualities

• Comfortable working in a fast-paced, evolving environment
• Structured, analytical thinker with strong ownership mindset
• Curious, proactive, and focused on impact rather than just analysis

Xometry is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

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