Senior Data Science Manager

Mercury Mercury · Fintech · Remote · Data Science

Senior Data Science Manager to lead a team supporting Go-To-Market functions, growth product experimentation, and core product experiences in a fintech company. The role focuses on defining measurement and experimentation strategy, elevating analytical craft, and building scalable analytics systems for AI-enabled insights.

What you'd actually do

  1. Lead and develop a team of Data Scientists embedded across go-to-market, growth product, monetization, and core product experiences
  2. Define the measurement and experimentation strategy across the customer lifecycle — from acquisition and activation to monetization, expansion, and retention — ensuring our investments are grounded in rigorous analysis and trusted data
  3. Elevate the craft of experimentation, pricing and monetization analytics, and commercial performance measurement, balancing analytical precision with decision velocity
  4. Partner closely with Product, Marketing, Sales, and Finance to shape roadmaps, evaluate ROI, and guide revenue forecasting and capital allocation decisions
  5. Translate complex quantitative signals into clear insights that influence product direction and revenue strategy

Skills

Required

  • 10+ years of experience
  • 3+ years leading high-performing data teams
  • Deep experience in growth, monetization, and product analytics
  • Proven track record of partnering with Product, Marketing, Sales, and Finance to shape roadmaps and drive revenue outcomes
  • Strong business judgment
  • Ability to balance analytical rigor with decision velocity in high-impact commercial environments
  • Highly fluent in experimentation design, attribution, and causal inference
  • Ability to raise the bar on analytical craft across a team of senior ICs
  • Experience building scalable analytics frameworks and self-serve capabilities that increase leverage and support AI-enabled insight generation within growth and product domains
  • Thrive in ambiguity, setting clear priorities that balance user value, growth, and long-term business impact

What the JD emphasized

  • AI-enabled insights
  • experimentation design
  • attribution
  • causal inference
  • scalable analytics frameworks
  • self-serve capabilities