Lead Data Scientist

Versant•Universal City, CA

1d•Hybrid

About The Position

We're looking for a Lead Data Scientist to be the senior technical leader for our Fandango and Rotten Tomatoes analytics organization. You'll build the models, frameworks, and processes that transform how we deliver data products to the business. You'll own our core modeling work: segmentation, customer lifetime value, retention, and forecasting. You will set the methodological standard for how we run experiments. You'll also bring modern AI technical leadership to how we build and ship analytics. This is a senior individual contributor role with organization-wide influence and no direct reports, reporting to the head of the Fandango and Rotten Tomatoes Analytics.

Requirements

A degree in a quantitative or technical field, or equivalent practical experience.
7+ years in data science or analytics, including leading technical work as an individual contributor and guiding data-informed strategy.
Hands-on expertise in predictive modeling and machine learning, with experience building segmentation, lifetime-value, and forecasting models.
Strong command of experimentation and causal inference, and experience setting those standards for an organization.
Expert SQL and a modern data science toolkit, with a solid grasp of pipelines and data modeling.
Technical leadership experience, including AI technical leadership.
Experience integrating AI tools to redesign workflows and drive measurable impact.
Proven success working across Product, Engineering, Marketing, and Finance.
Exceptional problem framing and communication skills.
Ability to turn complex modeling into recommendations senior leaders act on.
A track record of mentoring and raising the technical capability of other analysts or scientists.

Nice To Haves

Experience in media, streaming, entertainment, or consumer subscription and transactional businesses.
An advanced degree in a quantitative field.

Responsibilities

Build models, frameworks, and processes to transform data product delivery.
Own core modeling work including segmentation, customer lifetime value, retention, and forecasting.
Set the methodological standard for running experiments.
Provide AI technical leadership for building and shipping analytics.
Develop frameworks, reusable models, and standards to raise the rigor and speed of the team's work.
Move towards durable, scalable, self-service data products.
Identify where AI can redesign analytical workflows, build solutions, and deliver measurable gains.
Champion sound AI practices, risk assessment, and bias mitigation.