tatari
Data Science Manager
At a Glance
- Location
- San Francisco, California, United States
- Work Regime
- hybrid
- Experience
- 2+ years
- Compensation
- Benefits: Competitive salary ($140,000 - $180,000/annually) Equity compensation
- Posted
- 2026-03-04T17:17:49-05:00
Key Requirements
Required Skills
Domain Knowledge
- Cloud
- Engineering
- Media
Benefits & Perks
reimbursement Unlimited PTO and sick days Monthly Company
Equity compensation Health insurance coverage for you and your dependents 40
Requirements
Master’s degree or equivalent experience in a technical field (e.g., Computer Science, Mathematics, Physics, Data Science, etc.)
Minimum 2 years of management experience, plus minimum of 6 years of experience in a Data Science role, or a related role in a technology environment
Strong strategic thinking and project management skills, including priority and dependency management across several large projects with multiple stakeholders
Strong organizational and leadership skills, able to exercise judgment to make tradeoffs through cost-benefit analysis
Ability to translate complex concepts to both technical and non-technical stakeholders and drive alignment across Product, Engineering, and Data Science
Proven ability to lead through ambiguity and move from 0→1 with new features or systems
Compensation & Benefits
Competitive salary ($140,000 - $180,000/annually)
Equity compensation
Health insurance coverage for you and your dependents
401K, FSA, and commuter benefits
$150 monthly spending account
$1,000 annual continued education benefit
Responsibilities
Manage and grow a team of data scientists focused on AI/ML product development
Facilitate the team’s design of algorithms and models to innovate, research, and deliver on product features that drive and expand our business opportunities
Empower the team to innovate and explore new methodologies and improve upon existing algorithms
Actively develop strong working relationships across Product, Engineering, and Infrastructure teams to foster alignment, drive shared decision-making, and accelerate delivery of AI-powered features
Collaborate with partner teams to define scope, translate product requirements into technical approaches, and ensure successful delivery
Own Data Science prioritization for AI/ML outcomes, allocate resources and manage workload