tatari
Data Science Manager
At a Glance
- Location
- Los Angeles, California, United States
- Work Regime
- hybrid
- Experience
- 2+ years
- Compensation
- Benefits: Competitive salary ($140,000 - $180,000/annually) Equity compensation
- Posted
- 2026-03-04T17:17:37-05:00
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
Ability to translate complex concepts to both technical and non-technical stakeholders and drive alignment across Product, Engineering, and Data Science
Advanced knowledge of statistics and probability, particularly their application in model development
Intermediate to advanced experience in Data Manipulation Software (e.g.
SQL, Python), cloud computing environments (e.g., AWS), and ML workflows or toolkits
Demonstrated experience in leading AI work, ideally including work with LLMs, embeddings, or ranking/recommender systems – with a successful track record of end-to-end implementation.
TV Media Industry or Ad Tech experience a plus
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