Sr. Data Scientist in Oak Brook

We are seeking an experienced Data Scientist with deep expertise in Design of Experiments (DoE) to drive how we design, execute, and measure operational pilots across our network of parts warehouses. In this role, you'll help architect the backbone of our test-and-learn strategy - identifying matched-market branches, structuring controlled pilots, and ensuring we can confidently separate signal from noise.

Our operating environment involves complex industrial distribution networks, where interventions may occur at the branch level, the SKU level, or both. You'll design experiments where some branches or SKUs receive a treatment while others serve as control - requiring careful matching, robust measurement frameworks, and multi-factor experimental design to uncover both main and interaction effects. An Operations Research background is a strong plus, as many experiments involve operational levers, supply chain flows, and optimization problems.

Responsibilities

• Architect and operationalize structured experimental designs across branches, customers, and SKUs to evaluate strategic initiatives, pricing strategies, stocking decisions, operational changes, and commercial programs.
• Develop branch-level experimental frameworks using matched market methodology (e.g., pairing or clustering similar branches) to ensure clean test-control comparisons and high-quality inference.
• Implement true DoE frameworks that go beyond simple A/B testing - including factorial and fractional factorial designs, response surface methods, and multivariate test structures - to explore multiple operational or commercial levers simultaneously and understand their interaction effects.
• Create within-branch experimental setups (e.g., testing interventions on some SKUs while holding others as controls) to isolate effects in complex operational settings.
• Build robust measurement frameworks for pilots - including KPI selection, metric design, tracking dashboards, and real-time monitoring to assess pilot fidelity and performance.
• Apply advanced statistical and causal inference techniques (e.g., synthetic control methods, diff-in-diff, regression modeling) to extract insights and quantify impact from noisy real-world data.
• Partner cross-functionally with operations, merchandising, branch leadership, and product teams to identify pilot opportunities, define treatments, ensure operational feasibility, and guide interpretation.
• Create DoE playbooks and standardized pilot protocols to scale structured testing across the organization.
• Use operations research methods - optimization, simulation, probabilistic modeling- to enhance pilot designs and anticipate system-level effects.

• Background in Operations Research, including network optimization, simulation, or queuing theory.
• Experience applying causal inference methods in non-laboratory settings (e.g., synthetic control, difference-in-).
• Experience in B2B or industrial distribution environments.
• Familiarity with cloud data and compute platforms (e.g., Databricks, Snowflake, AWS, Azure).
• Strategic thinker with strong business and technical acumen.
• Excellent communication and collaboration abilities, with the capacity to influence across all levels of the organization. This also includes the ability to translate highly-technical topics for non-technical leaders in order to identify needs and obtain alignment on decisions.
• High integrity, professionalism, and commitment to company values.
• Adaptable and resilient in a fast-paced, evolving technology landscape.
• Collaborative and influential, able to drive alignment across diverse teams and stakeholders.
• Adaptable, resilient, and comfortable leading and supporting change as well as clarifying action in the face of ambiguity.

Requirements

• 5+ years of relevant experience in data science, experimentation, or statistical modeling.
• Deep expertise in Design of Experiments, including multi-factor and multivariate designs (factorial, fractional factorial, response surface methods), matched markets, and A/B/n testing.
• Experience structuring experiments in operational or distribution network settings (e.g., branch or location-based pilots).
• Proven ability to identify and match test and control groups in heterogeneous environments.
• Strong statistical programming skills in JSL, Visual Basic, Python or R; proficiency in SQL and modern data platforms.
• Demonstrated ability to set up and monitor experiments end-to-end, from design through execution and impact measurement.
• Strong communication skills and ability to work across operational and business teams., * Advanced degree (M.S. or Ph.D.) in Statistics, Operations Research, Data Science, Economics, Industrial Engineering, Quality Engineering or a related quantitative field.

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