Senior Data Scientist

Our client, a fast-growing consumer digital product company, is hiring a Senior Data Scientist to build machine learning that decides what users see and do next in the product.

This is applied, production work: take proven models, adapt them to the company's behavioral data, ship them to real users, and show the lift. You will own personalization and recommendation systems end to end, from raw behavioral data to live serving, measured by retention, conversion, and lifetime value.

What you'll do

• Own the models that decide what each user sees next, how it is ranked, when messages and prompts appear, and which offer each user gets.
• Build predictive models for retention, churn, and conversion, with predicted lifetime value as a key focus.
• Adapt and fine-tune existing architectures to the company's data, rather than building from scratch.
• Take models to production and own them: serving, monitoring, retraining.
• Design and read A/B and causal experiments that prove real business impact.
• Work with product, content, and growth teams, and replace hand-made rules with models that learn.

What we're looking for

• BSc in a quantitative field (Data Science, Computer Science, Statistics, Mathematics). MSc or PhD is a strong advantage.
• 5+ years in data science or machine learning, with models shipped to production.
• Hands-on experience building recommendation and/or personalization systems in production. This is essential.
• Practical experience training and adapting deep learning models for production.
• Practical experience with time series data: forecasting, seasonality, noisy signals.
• Solid applied ML foundations, and engineering skills to keep deployed models reliable.
• Rigorous experimentation: A/B testing, causal thinking, honest measurement.
• Strong SQL and comfort with large data warehouses and raw event data.

Advantage

• Large-scale recommender systems (retrieval and ranking, sequence models, embeddings).
• LTV, churn, or propensity modeling in a consumer or subscription business.
• Streaming, entertainment, or mobile consumer apps.
• MLOps and causal inference / uplift modeling.