Senior Applied Scientist

8+ years in data science or applied machine learning roles, with demonstrated experience in causal inference and building and deploying models
Graduate degree (M.S. or Ph.D.) in Statistics, Econometrics, Computer Science, Machine Learning, Mathematics or a related field
Proven experience applying causal inference methods (experimental and observational) as well as ML methods including: Experimentation (A/B testing), Observation Studies (propensity score matching, difference-in-differences, synthetic controls, etc.), Statistical modeling, ML methods, Ranking & Recommendation Systems
Strong proficiency in Python and ML libraries (e.g., Scikit-Learn, Pandas, Numpy)
Solid SQL skills
Familiarity with ML frameworks such as PyTorch and TensorFlow
Experience with Google Cloud Platform or AWS preferred, with BQML or Spark as a plus.

Apply causal inference methods including experimentation (A/B testing) as well as observational methods (propensity score matching, difference-in-differences, synthetic controls, etc.) to measure the performance of our models and the impact our models have for our customers.
Analyze large datasets to support machine learning models, perform feature engineering, and develop and apply machine learning models to our backend and frontend applications.
Evaluate and validate model performance, using metrics to improve accuracy and reliability in production environments.
Work with data engineering, product, software engineering, and UX teams to integrate models into workflows, ensuring solutions align with business needs.
Present analysis and model results to technical and non-technical stakeholders, using data visualizations and clear explanations to convey insights effectively.