Business Data Science Manager

WD-40 COMPANY•San Diego, CA

8h•Remote

About The Position

This role is for a highly skilled individual with 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 data infrastructure, and this role will involve significant 'plumbing' work for the first 6-12 months before focusing on advanced modeling. The position offers genuine autonomy, exposure to senior leadership, the opportunity to ship models, and a supportive director. It is a remote role based in the US, requiring authorization to work without visa sponsorship.

Requirements

Pragmatic approach to modeling, selecting the simplest effective model.
Proficiency in Python and SQL for data analysis and model building.
Ability to communicate data findings ethically and assertively to leadership.
Low ego and high collaboration skills, thriving in a team environment.
Curiosity and self-sufficiency in learning new technologies.
Resilience in handling ambiguity and deriving clear answers from vague questions.
Bachelor's degree in a STEM field (math, statistics, computer science, physics, data science, or similar).
5+ years of experience preferred, but exceptional candidates with 2+ years will be considered.
Familiarity with Python, SQL, Microsoft Fabric, and D365.

Nice To Haves

Commercial fluency, including understanding of CPG, finance, or sales analytics, margin, trade spend, and retailer perspectives.
Master's degree in a STEM field.
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 data infrastructure foundation.
Communicate effectively with stakeholders to maintain buy-in during the infrastructure build.
Translate complex analytical outputs into clear, concise executive narratives, leading with decisions rather than methodology.
Present findings to various audiences, from VPs to data engineers, adapting communication style as needed.
Coach Sales Insights & Analytics Managers on understanding model outputs and communicating insights to the business.