Data Scientist II, Hyderabad
Warner Bros. (Warner Bros. Entertainment Inc.)Match Score
Against your primary resumeLogin to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Protocol Intelligence
Data-driven signals on your job's competitivenessStrong employer brand, mid-level data scientist title, metro location, and broad skillset make competition high.
Core ML, Python, Spark, and productionization skills transfer across industries, though streaming/ad-tech expertise improves fit.
Explicit 3–5 years plus required ML modeling, productionization, and specific platform experience makes shortlisting stringent.
Job Description
Structured overview of role & requirementsAbout This Role
Lead the design and development of data science models addressing identity resolution, audience segmentation, personalization, and engagement across Warner Bros. Discovery's global streaming platforms.
Design experiments and evaluation frameworks to measure model quality and business impact, ensuring data science solutions are actionable and measurable.
Collaborate with Machine Learning Engineering to operationalize models into scalable production pipelines, maintaining standards for reproducibility, monitoring, and model health.
Minimum Requirements
3–5 years of hands-on experience building data science, statistical modeling, or machine learning solutions for real-world products or business systems.
Strong programming and analytical skills in Python and SQL; experience with Spark or large-scale distributed data processing preferred.
Experience with Databricks, Snowflake, AWS, MLflow, and related data/ML tools.
Work Experience Required: 3–5 years relevant data science experience. Notice period: Not explicitly mentioned in the JD.
Ideal Candidate Profile
Experienced in framing ambiguous business problems into measurable data science hypotheses and actionable insights impacting product and business metrics.
Preference for candidates with background in streaming, media, advertising technology, personalization, forecasting, or consumer identity domains.
Collaborative operator skilled at communicating complex technical methods and trade-offs to both technical and non-technical stakeholders in a global organization.