
Data Scientist, Growth
Lead data science for major growth areas (acquisition, activation, recommender systems). Design experiments and metrics for fast decision-making (e.g., CUPED, sequential testing) and use quasi-experimental methods when randomization isn't feasible. Define personalization and recommendation evaluation, model freemium economics (credit consumption, conversion, pricing effects), and develop behavioral/content-based segmentation (embeddings, clustering). Partner with Product, Engineering, and Data Engineering to build metrics, datasets, and experimentation tooling. Requires 6+ years in data science, strong experimental design and statistical inference, and expert SQL and Python.