Business Data Science Manager

WD-40 Company•San Diego, CA

19h•Remote

About The Position

This role is for a highly skilled individual who possesses both strong data science capabilities (Python, ML, statistical modeling) and genuine commercial understanding (trade ROI, margin analysis, retailer P&Ls). The company is building its commercial data science function from the ground up, with the data infrastructure currently a work in progress. The initial 6-12 months will involve significant data plumbing before advanced modeling can take center stage. This position offers genuine autonomy, direct exposure to senior leadership, the opportunity to ship models into production, and a supportive manager. It is a remote role, open to candidates anywhere in the US, with no visa sponsorship offered.

Requirements

Proficiency in Python and SQL.
Ability to select and implement the simplest effective model for a given question.
Strong ethical compass; ability to present data-driven findings honestly to leadership.
Low ego and high collaboration skills; comfortable working in a team environment.
Demonstrated curiosity and self-sufficiency in learning new technologies.
Resilience in ambiguous situations; ability to derive clear answers from vague requests.
Bachelor's degree in a STEM field (math, statistics, computer science, physics, data science, or similar).
Minimum of 2 years of experience, with 5+ years preferred for exceptional candidates.
Familiarity with Python, SQL, Microsoft Fabric, and D365.

Nice To Haves

Master's degree in a STEM field.
Commercial fluency, including understanding of CPG, finance, or sales analytics, margin, trade spend, and retailer operations.
Familiarity with Power BI or Tableau.

Responsibilities

Build and scale models including time series forecasting, price elasticity, market mix modeling, channel optimization, and ML-based attribution using Python.
Perform data cleaning, structuring, and governance to build the foundational data infrastructure.
Communicate effectively with stakeholders to maintain buy-in during the infrastructure development phase.
Translate complex analytical outputs (e.g., regression results) into clear, concise executive narratives, leading with decisions rather than methodology.
Present findings to various audiences, from VPs seeking answers to data engineers requiring technical details.
Coach Sales Insights & Analytics Managers on understanding model outputs and communicating insights to the business.
Conduct advanced analytics and model building (~50% of time).
Contribute to data infrastructure and strategy (~20% of time).
Engage in business partnership and storytelling (~20% of time).
Provide omni-channel and e-commerce insights (~10% of time).