tatari

Data Science Manager

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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

AWSData SciencePythonSQL

Domain Knowledge

  • Cloud
  • Engineering
  • Media

Benefits & Perks

Time Off

reimbursement Unlimited PTO and sick days Monthly Company

Health Insurance

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