Senior Manager, Marketing Decision Science

The role

This role runs the marketing decision science group at a grocery delivery company, embedding analysts across paid media, search optimization, and retailer marketing. The manager sits within the analytics organization, reports to its director, and regularly presents to senior marketing and finance leaders during planning and performance reviews.

What you would do

• Coach and grow a five-person group of scientists and analysts, setting quality standards and guiding daily priorities.
• Run paid marketing analytics end to end, covering channel goals, budget pacing, lift testing, and retention analysis.
• Coordinate how media mix models, lift experiments, lifetime value, and multi-touch attribution fit into one measurement approach.
• Lead re-goaling and scenario planning that shapes how marketing dollars get distributed.
• Serve as the main analytical contact for senior marketing and finance stakeholders, presenting at business reviews and pacing meetings.
• Set the team's roadmap alongside the director and turn business priorities into ordered analytical work.
• Collaborate with the broader data science group on measurement design, lifetime value models, and tracking infrastructure.
• Automate recurring reporting and build self-serve tools to reduce manual workload.
• Plan staffing coverage in advance, including during periods of reduced availability.

What they asked for

• 8+ years in data science, analytics, or quantitative work, with 3+ years leading analysts or scientists.
• Deep grounding in paid marketing measurement, including incrementality, media mix modeling, attribution, and LTV optimization.
• Strong SQL skills and comfort validating data directly rather than relying on summaries.
• Manages senior cross-functional relationships independently and represents analytics in leadership forums.
• Proven at turning complex analysis into investment recommendations for non-technical audiences.
• Experience developing decision scientists or analysts who are held to high quality expectations and receive direct feedback.

Nice to have

• Background at a consumer marketplace or fast-growing technology company.
• Familiarity with geo experiments, Bayesian approaches, or causal inference methods.
• Hands-on work with LTV-driven media optimization and budget allocation models.
• Experience building or rebuilding channel reporting and pacing systems.