Principal Data Scientist
Tasks
- Build optimization and prescriptive analytics
- Define data science technical architecture
- Define proxy measurements and assumptions
- Design and analyze experiments
- Detect anomalies
- Develop causal graphs
- Develop measurement frameworks and proxy metrics
- Drive CI CD and version control for data science
- Embed data science in product lifecycle decisions
- Ensure high quality SSOT data
- Implement predictive and prescriptive time series forecasting
- Mentor data scientists and review methodological rigor
- Own data science roadmap
- Perform causal inference and incrementality testing
- Prototype and productionize predictive models
- Prototype and productionize prescriptive systems
- Set standards for ML reproducibility testing and monitoring
Perks/Benefits
- Culture of innovation
- Customer-centric culture
- Exceptional people and team
- Real impact
- Relentless growth
Skills/Tech-stack
Anomaly Detection | Bayesian Inference | CI/CD | Causal Graphs | Causal Inference | CausalImpact | CausalML | Containers | DBT | Dataform | Distributed Computing | DoWhy | EconML | Experimentation | Incrementality Measurement | LightGBM | MLOps | Machine Learning | Marketing Mix | Marketing Mix Modeling | Mix Modeling | Model Monitoring | Optimization | Predictive Analytics | Prescriptive Analytics | PyTorch | PyWhy | Python | SciPy | Scikit-learn | Series forecasting | StatsModels | TensorFlow | Testing | Time Series | Time Series Forecasting | Version control | XGBoost
Education
N/A
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