Applied Data Scientist, Personalization & Machine Learning

What you'll do

• Identify and prioritize product opportunities, define model-informed decisions, and build datasets, features, training workflows, and models.
• Translate predictions into product actions; design experiments and apply causal reasoning to assess outcomes.
• Partner with engineering on integration, scoring, monitoring, rollout, and iteration; explain results and document reusable methods.

What they ask for

• Effective Python and SQL skills, including investigating imperfect data and debugging models.
• Experience building and evaluating applied ML systems that influence a product or business decision, with ownership of modeling work.
• Ability to build and schedule production scoring pipelines using dbt, airflow, and flyte.
• Understanding of supervised learning, evaluation, experimentation, and the distinction between predicting an outcome and changing it.

Nice to have

• Experience with recommendation systems, personalization, growth, retention, lifetime value, or treatment-effect modeling.

Pay

$347K – $445K (base salary or total pay not specified)

Tools

Python, SQL, dbt, airflow, flyte

AI focus

Develop and deploy machine-learning models and product interventions, including personalization, recommendations, lifecycle management, promotion targeting, retention, and customer lifetime value.