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Senior Data Scientist, Recommendations and Personalization
Не указаноПолная занятостьData ScienceEntertainment
Own applied machine learning for recommendations and personalization, from behavioral data and model adaptation through production deployment and impact measurement.
Обязанности
- Own recommendation and personalization across the user journey and product surfaces
- Build predictive models for retention, churn, conversion propensity, and lifetime value
- Adapt existing models and architectures to the company’s data
- Deploy and own models in production, including serving, monitoring, retraining, and iteration
- Design and analyze experiments to measure business impact
- Partner with Product, Content, and Growth teams to apply model outputs
- Replace manually tuned heuristics with learned models
Требования
- BSc in a quantitative field such as data science, computer science, statistics, or mathematics
- At least 5 years of data science or machine learning experience, including production model deployment
- Hands-on production experience building recommendation or personalization systems
- Strong applied machine learning skills, including modeling, evaluation, and feature engineering
- Hands-on experience training and adapting neural networks and deep learning models for production
- Experience modeling time series, including forecasting, trends, seasonality, and noisy data
- Ability to adapt existing models or architectures to real-world data
- Experience deploying and maintaining production models
- Knowledge of A/B testing, causal reasoning, and measurement
- SQL skills and experience working with large data warehouses
Будет плюсом
- MSc or PhD
- Experience with recommendation systems at scale, including retrieval, ranking, sequence or session models, and embeddings
- Consumer or subscription experience with LTV, churn, or propensity modeling
- Streaming, entertainment, consumer mobile, or growth-marketing data experience
- MLOps experience with feature stores, serving, monitoring, or retraining pipelines
- Causal inference or uplift modeling