Senior Data Scientist
Location: Tel Aviv, Israel (תל אביב) · Category: R&D · Seniority: Senior · Work model: Hybrid · Posted: 2026-10-11
The company
We are a fast-growing B2C entertainment startup building a mobile-first streaming product for on-demand viewing. We pair original content with a data- and AI-led discovery experience, and we are scaling quickly with a large and growing international audience.
About the Role
A consumer digital product company is hiring a Senior Data Scientist to build machine learning that shapes what users see and do next in the product. The work is hands-on and production-focused, not dashboards or academic research. You will start from well-established models, tailor them to our behavioral data, release them to real users, and show the measurable impact.
You will be responsible for personalization and recommendation systems from start to finish: from raw user-behavior data to live model serving, guided by business outcomes such as retention, engagement, conversion, and lifetime value.
The role suits someone who wants their models to run in a live product and influence real user behavior, who is comfortable owning a problem from data through deployment, and who is rigorous about measuring results.
Responsibilities
- Personalization & Recommendations: Own the models that decide what content each user is shown next, how it is ranked on screen, when and how messages are sent, when in-product prompts appear, and which offer a given user receives, extending to new surfaces as the product grows.
- Predictive Modeling: Build models from raw behavioral data that predict retention, churn, and likelihood to convert. Predicted customer lifetime value is a key focus, both to drive personalization and as a quality signal for marketing partners.
- Adapting Existing Models: Start from proven architectures and fine-tune or adjust them to our data, favoring practical application over building from scratch.
- Production Ownership: Take models live and stay responsible for them: serving, monitoring, retraining, and continuous improvement.
- Experimentation: Design and analyze A/B and causal experiments to demonstrate genuine business impact beyond offline metrics.
- Collaboration: Work with product, content, and growth teams so that model outputs turn into concrete decisions.
- Replacing Heuristics: Move hand-crafted rules over to models that learn from data.
Requirements
- Bachelor's degree in a quantitative discipline such as Data Science, Computer Science, Statistics, or Mathematics; a Master's or PhD is a strong advantage.
- At least 5 years of professional experience in data science or machine learning, including deploying models to production.
- Proven hands-on experience building recommendation and/or personalization systems that run in production; this is essential.
- Solid grounding in applied machine learning: modeling, evaluation, and feature engineering.
- Practical experience training and adapting deep learning models for production use.
- Practical experience with time series data, including forecasting, trend and seasonality analysis, and working with noisy signals.
- Ability to take an existing model or architecture and make it work on imperfect, real-world data.
- Engineering skills to keep deployed models reliable and keep improving them.
- Disciplined approach to experimentation: A/B testing, causal thinking, and honest measurement.
- Strong SQL skills and ease working with large data warehouses and raw event data.
- Clear communicator with a drive to deliver.
Nice to Have
- Experience with large-scale recommender systems: candidate retrieval and ranking, sequence or session-based models, embeddings.
- Lifetime value, churn, or propensity modeling in a consumer or subscription business.
- Background in streaming, entertainment, mobile consumer apps, or growth marketing data.
- MLOps experience: feature stores, model serving, monitoring, and retraining pipelines.
- Knowledge of causal inference or uplift modeling.
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