Data Product Scientist

AMC Networks•New York, NY

1d•$135,000 - $150,000•Hybrid

About The Position

AMC Global Media is home to many of the greatest stories and characters in TV and film and the premier destination for passionate and engaged fan communities around the world. We create and curate celebrated series and films across distinct brands and make them available to audiences everywhere. Our portfolio includes targeted streaming services AMC+, Acorn TV, Shudder, Sundance Now, ALLBLK and HIDIVE; cable networks AMC, BBC AMERICA, Independent Film Company, Sundance TV and We TV; and film distribution labels Independent Film Company and RLJE Films. The company also operates AMC Studios, our in-house studio, production, and distribution operation behind acclaimed and fan-favorite originals including The Walking Dead Universe and the Anne Rice Immortal Universe, and AMC Networks International, our international programming business. We are currently seeking a Data Product Scientist to join our Business Intelligence based in our New York, NY offices.

Requirements

6+ years of experience in applied data science.
Bachelor’s Degree in STEM or Quantitative field, or 4 additional years of relevant work experience.
Deep hands-on experience in applied data science: working with complex datasets, modeling, evaluation, and communicating results.
Strong coding ability (Python and SQL expected; experience with production-grade analytics code preferred).
Strong understanding of ML fundamentals and model evaluation; ability to choose approaches appropriately and explain tradeoffs clearly.
Ability to work independently, manage ambiguity, and drive end-to-end delivery for scoped problem areas while partnering effectively with engineering and product.
Strong communication and interpersonal skills; proven ability to work across technical and non-technical audiences.

Nice To Haves

Master’s Degree in STEM of Quantitative field.
Experience building solutions intended for product scale (deployment considerations, monitoring, reliability, documentation, adoption).
Experience with GenAI approaches (prompting patterns, evaluation, safety/quality considerations) applied to business use cases.
Experience in a “data products” environment where research becomes reusable, governed, and enabled across teams.

Responsibilities

Work directly within our databases to understand data structure, mappings, lineage, quality issues, and bias; document findings and implications for analysis and product decisions.
Write scripts to extract, transform, and analyze data to test hypotheses and validate business ideas.
Build, test, and compare ML approaches (including GenAI where appropriate), selecting methods based on the business goal, data constraints, and evaluation criteria.
Design model evaluation strategies and clearly communicate model limitations, confidence, and tradeoffs.
Create reproducible workflows suitable for productization.
Contribute to and help uphold standards for analytical rigor, reproducibility, and evaluation.
Partner with stakeholders to understand business objectives and convert them into well-scoped data science questions.
Translate results into decision-ready insights: what we learned, why it matters, what actions it supports, and what risks/limitations exist.
Support clear storytelling and recommendations grounded in evidence, tailored to the audience.
Use data, research, and models to help design and develop scalable data products.
Create product briefs, requirements, and documentation that bridge business needs with technical implementation.
Partner with engineering to develop efficient data solutions.
Plan for future scalability (data volume growth, performance, monitoring needs, governance considerations).
Train business users on how to interpret metrics, perform reporting, and convert data into insights and decisions.
Build enablement materials (playbooks, quick-starts, definitions, case studies) to drive adoption and consistent usage.
Partner with teams to understand incoming demand and support scoping, triage, and prioritization across multiple workstreams.
Bring order and clarity through scoping, sequencing, and stakeholder alignment, without losing technical depth.