Senior Data Scientist

A national industrial distribution organization is seeking a Senior Data Scientist (Operations & Supply Chain) to lead and scale a best-in-class experimentation and test-and-learn strategy across its parts distribution warehouse network. This highly visible role will focus on designing, executing, and measuring complex operational pilots that inform pricing, inventory, and supply chain decisions at scale. In this role, you will apply advanced Design of Experiments (DoE) and causal inference techniques in real-world, noisy environments where interventions may occur at the branch, customer, or SKU level. You will partner closely with operations, merchandising, pricing, and product teams to ensure experiments are statistically rigorous, operationally feasible, and actionable.

Required Skills & Experience

5+ years of experience in data science, experimentation, or statistical modeling

Deep expertise in Design of Experiments, including:

Factorial and fractional factorial designs

Response surface methodologies

Matched-market testing

A/B/n and multivariate experimentation

Proven experience designing and executing experiments in operational or distribution network environments (e.g., branch- or location-based pilots)

Strong statistical programming skills in Python or R; proficiency in SQL and modern data platforms

Hands-on experience applying causal inference methods in real-world, non-laboratory settings

Preferred Skills & Experience

Advanced degree (M.S. or Ph.D.) in Statistics, Data Science or a related quantitative field

Background in Operations Research, including optimization, simulation, or queuing theory

Experience in B2B, industrial distribution, logistics, or supply chain environments

Familiarity with cloud-based data and compute platforms such as Databricks, Snowflake, AWS, or Azure

Experience developing standardized experimentation frameworks or playbooks

Strong executive communication and stakeholder influence skills

What You Will Be Doing

35% Experimental Design & Methodology (DoE, matched markets, multivariate testing)

25% Measurement, Causal Inference & Impact Quantification

20% Operations Research & System-Level Modeling

20% Cross-Functional Strategy & Experimentation Enablement

Daily Responsibilities

Architect and operationalize structured experimental designs across branches, SKUs, and customers to evaluate pricing strategies, stocking decisions, and operational changes

Design branch-level pilots using matched-market methodologies to ensure clean test/control comparisons and strong causal inference

Implement advanced DoE frameworks beyond basic A/B testing, including factorial designs and response surface methods

Develop within-branch experimental designs (e.g., SKU-level treatments with internal controls) to isolate effects in complex operational environments

Build and maintain robust pilot measurement frameworks, including KPIs, dashboards, and real-time monitoring

Apply statistical and causal inference techniques such as difference-in-differences, synthetic controls, and regression modeling to quantify results

Partner with operations, merchandising, pricing, branch leadership, and product teams to identify opportunities, define treatments, and interpret results

Create standardized DoE playbooks and pilot protocols to scale experimentation across the organization

The Offer

Competitive Salary + 401(k)

Health, Dental, Vision Insurance, and Life Insurance

PTO

Applicants must be currently authorized to work in the United States on a full-time basis now and in the future.

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