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Technology And Operations - Data Scientist I
Career Insights for Natural Language Processing Engineer
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What they do
A Natural Language Processing Engineer specializes in developing and implementing algorithms and models tailored for understanding, processing, and generating natural language text. They utilize methodologies such as tokenization, parsing, named entity recognition, part-of-speech tagging, and other NLP techniques to perform tasks including text classification, chatbot development, and other applications where the primary input or output is natural language text.
$158,568 / year median in California
Job Description
Request Title:
Manager of Data ScienceBusiness Unit/Group:
Category ManagementRequisition Number:
38119-1Intended Start Date:
8/13/2026Contract Duration:
3-months Possibility For Extension / Conversion? Possible Max Hourly Pay Rate OT Required / Expected? No WB Games Resource(s)? No CNN Resource(s)? No Open to former interns? Yes What We Do/Project Warner Bros. Discovery is seeking a hands-on data scientist to fill the Manager of Data Science position within our Sales organization's Data Science team. Our Data Science team works very closely with Sales and Marketing, and in this position you will maintain and extend existing statistical and machine-learning work that informs how we window, price, and forecast content — including title-level release windowing, price optimization, and demand/revenue forecasting. Working under the direction of the VP of Data Science, you will pick up in-flight models and dashboards and keep them running and accurate. You should be comfortable in a fast-moving environment and able to balance scheduled deliverables with ad-hoc analytical requests. Job Responsibilities / Typical Day in the Role Data Analysis & Modeling- Maintain, validate, and extend existing forecasting, pricing, and windowing models built in Python (pandas, scikit-learn, statsmodels) and SQL against Snowflake.
- Write clean, reproducible, version-controlled code (Git).
- Automate recurring data extraction and preparation pipelines to reduce manual effort.
- Assess model quality with appropriate validation (holdout/backtesting) and error metrics (e.g., MAPE, RMSE, bias), and flag when a model needs retraining or rework.
- Turn analysis into clear, decision-ready storylines — visual and written — for non-technical audiences. Applying AI & GenAI in the Workflow
- Use AI-assisted development tools (e.g., Cursor, GitHub Copilot) to accelerate coding, refactoring, and debugging while keeping a human-in-the-loop review of all output.
- Apply large language models (LLMs) to practical, day-to-day tasks: summarizing datasets and results, drafting documentation, generating and explaining SQL/Python, and accelerating exploratory analysis.
- Critically evaluate AI output for correctness, and follow enterprise data-privacy, security, and approved-tool guidelines when using AI on WBD data. Visualization & Application Development
- Build and maintain Tableau dashboards that monitor content, customer, and revenue trends for Sales, Marketing, and other business stakeholders.
- Develop self-serve interactive tools (Streamlit and/or Tableau) so partner teams can explore results without analyst support.
- Create views of model diagnostics and archived results so the team can track model performance over time. Collaboration & Mindset
- Partner day-to-day with Sales and Marketing to understand their questions and deliver clear, useful analysis.
- Work under the direction of the Director and VP of Data Science, translating their priorities into delivered analyses.
- Pivot quickly between planned work and urgent ad-hoc requests without losing rigor.
- A strong communicator and eager learner — open to coaching and to actively growing and strengthening communication skills over the engagement.
Soft Skills:
1) Good written and verbal communication skills, with an openness to growing and strengthening them, and comfort explaining technical work to non-technical Sales and Marketing partners.Technology Requirements:
1) Python and SQL coding experience for data manipulation, analysis, and modeling. 2) Practical experience with core ML/statistics libraries (e.g., scikit-learn, statsmodels, pandas) and standard model-validation techniques. 3) Experience querying a cloud data warehouse (Snowflake preferred). 4) Basic working knowledge of Tableau — the team relies on it heavily to deliver insights to Sales and Marketing. 5) Comfort using and sound judgment about AI-assisted coding tools and LLMs in a professional setting. 6) Version control with Git and an ability to produce reproducible work.Education / Certifications
1) Bachelor's degree in Statistics, Data Science, Computer Science, Mathematics, Economics, or a related quantitative field. Interview Process / Steps 1) 1st round screening with Managers on the team. 2) 2nd round with Hiring Manager (Senior Manager) Additional Notes- Sourcing in CA - Burbank.
- Hybrid - 3-days onsite.