OVERVIEW OF THE COMPANY

Fox Corporation

Under the FOX banner, we produce and distribute content through some of the world’s leading and most valued brands, including: FOX News Media, FOX Sports, FOX Entertainment, FOX Television Stations and Tubi Media Group. We empower a diverse range of creators to imagine and develop culturally significant content, while building an organization that thrives on creative ideas, operational expertise and strategic thinking.

JOB DESCRIPTION

OVERVIEW OF THE COMPANY

Fox Corporation is home to industry-leading brands including FOX News Media, FOX Sports, FOX Entertainment, FOX Television Stations, Tubi Media Group, and FOX One, our recently launched direct-to-consumer (DTC) platform.

Across our portfolio, we build and operate large-scale digital platforms that serve millions of users globally. Our teams combine innovative technology, deep data insights, and world-class content to deliver highly personalized, reliable, and scalable consumer experiences across web, mobile, and connected TV platforms.

ABOUT THE ROLE

As a Senior Product Manager for the PNR (Personalization & Recommendations) team, you will act as the technical product lead for the intelligent systems that power discovery across Fox’s consumer platforms.

We are not just looking for a requirements gatherer; we need a practitioner who understands the "black box" of Machine Learning. You will work side-by-side with Data Scientists and ML Engineers to design and deploy sophisticated recommendation engines, re-ranking algorithms, and next-gen AI experiences.

You will be responsible for defining the logic behind why a user sees specific content, managing the feature engineering lifecycle, and leveraging GenAI/Prompt Engineering to innovate how users interact with our vast content library.

A SNAPSHOT OF YOUR RESPONSIBILITIES

• ML Product Strategy: Own the roadmap for Personalization and Recommendations, specifically focusing on algorithmic transparency, model efficacy, and user engagement metrics.

• Model Lifecycle Management: Partner deeply with Data Science to define model objectives, input signals, and success criteria. You will be involved in the details of Feature Engineering—determining which user behaviors and content metadata should feed into our training sets.

• Re-Ranking & Logic: Define and tune business logic for Re-rankers to balance user relevance with business objectives (e.g., promoting live events, surfacing diversity of content).

• GenAI & Prompt Engineering: Lead initiatives utilizing Large Language Models (LLMs). You will be hands-on with Prompt Engineering, iterating on prompts to refine search summaries, conversational discovery, or content metadata generation.

• Data-Driven Prioritization: Execute heavy hands-on data analysis (SQL, Python, or distinct analytics tools) to uncover opportunities. You will not rely solely on analysts; you will dig into the data yourself to validate hypotheses.

• Experimentation at Scale: Design rigorous A/B tests for algorithmic changes. You must be able to distinguish between signal and noise when analyzing how a new model variant impacts click-through and watch times.

• Technical Translation: Translate complex ML concepts into clear business narratives for executive stakeholders, explaining the "how" and "why" behind algorithmic decisions.

WHAT YOU WILL NEED

• Experience: 3–6+ years of Product Management experience, with at least 2+ years directly managing Machine Learning, Recommendation, or Search products.

• ML & Technical Fluency: Deep knowledge of Re-ranking architectures and recommendation algorithms (e.g., Collaborative Filtering, Matrix Factorization). You are comfortable driving Feature Engineering strategies alongside Data Scientists.

• GenAI & Prompt Engineering: Hands-on experience applying LLMs (e.g., GPT-4, Llama) to product features, specifically utilizing Prompt Engineering techniques to optimize outputs.

• Data & Experimentation: Advanced proficiency in SQL for independent analysis combined with rigorous experience designing backend A/B tests and interpreting statistical significance.

• Education: Bachelor’s degree in Computer Science, Engineering, Statistics, or equivalent practical experience.

NICE TO HAVE (BUT NOT REQUIRED)

• Experience with Vector Search and Embeddings.

• Background in Video Streaming, Media, or Content Discovery platforms.

• Experience building "Human-in-the-loop" ML systems.

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, gender identity, disability, protected veteran status, or any other characteristic protected by law. We will consider for employment qualified applicants with criminal histories consistent with applicable law.