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Senior Data Scientist – Data Platform Team
Warner Bros. DiscoverySenior Data Scientist developing machine learning models for data-driven solutions at Warner Bros. Discovery.
Tech Stack
Tools & technologiesKerasPythonPyTorchScalaScikit-LearnSparkTensorflow
About the role
Key responsibilities & impact- Design, develop, and deploy machine learning models and data science solutions at scale.
- Build ML capabilities using recommendation systems, personalization, predictive modeling, NLP, Generative AI, and Large Language Models (LLMs).
- Develop end-to-end ML workflows including data preparation, feature engineering, model training, validation, deployment, monitoring, and optimization.
- Apply advanced data science techniques including regression, classification, time series forecasting, causal inference, optimization techniques, and deep learning approaches.
- Build and improve scalable ML training and inference pipelines.
- Work closely with Data Engineers, ML Engineers, Product Managers, and business teams to deliver data-driven solutions.
- Perform experimentation, A/B testing, statistical analysis, and model evaluation to measure model effectiveness.
- Research and apply modern ML techniques including Transformers, Deep Learning models, NLP techniques, and AI-based solutions.
- Follow and promote data science best practices for model development, evaluation, and deployment.
- Mentor junior team members and provide technical guidance when required.
Requirements
What you’ll need- Bachelor’s/Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field.
- 5+ years of experience in building data science models and machine learning solutions.
- Strong understanding of statistics, machine learning algorithms, probability, and data science fundamentals.
- Hands-on experience in developing, deploying, and optimizing ML models in production environments.
- Strong programming experience with Python, R, Scala, or similar programming languages.
- Experience with ML frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, XGBoost, Keras, and Spark ML.
- Experience across the complete ML lifecycle, including feature engineering, training pipelines, model validation, deployment, monitoring, and continuous improvement.
- Experience working with large-scale datasets and distributed computing frameworks.
- Good understanding of MLOps concepts, scalable ML architectures, and production ML systems.
- Strong analytical thinking, problem-solving abilities, communication skills, and cross-functional stakeholder collaboration experience.
Benefits
Comp & perks- Equal opportunity employer
- Fast track growth opportunities
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
Machine LearningData ScienceFeature EngineeringModel TrainingModel ValidationPredictive ModelingNLP TechniquesDeep LearningStatistical AnalysisA/B Testing
Soft Skills
Analytical ThinkingProblem-SolvingCommunication SkillsCross-Functional Collaboration