Senior Data Scientist

Job Summary

The Senior Data Scientist designs and deploysstatistical, forecasting, and machine learning solutionsto solve complex business problems. This role works with cross-functional teams to buildproduction-ready models, generate actionable insights, and improve planning and decision-making at scale.

The role requires strong expertise instatistics, time series analysis, probabilistic forecasting, machine learning, and Google Cloud Platform (GCP).

Responsibilities

Statistical Modeling Forecasting

• Build and deploystatistical and probabilistic forecasting modelsfor demand, capacity, and trend analysis
• Applytime series methodsincluding regression-based models, ARIMA/SARIMA, and state-space models
• Define modeling assumptions, forecast uncertainty, and evaluation methods

Machine Learning Predictive Modeling

• Build and deploymachine learning modelsfor forecasting and prediction, including regression, tree-based models, gradient boosting and neural networks
• Selectstatistical or ML approachesbased on accuracy, interpretability, robustness, and operational needs
• Develop features and run experiments to improve model performance

MLOps Production Deployment

• Own themodel lifecycle, including development, backtesting, deployment, monitoring, and retraining
• Implementmodel monitoring, performance tracking, and data drift detection
• Ensure models areversioned, reproducible, and production-ready

Data Engineering Modeling

• Perform exploratory data analysis, feature engineering, and hypothesis testing on large, complex datasets
• Identify data requirements for forecasting and predictive modeling, including key inputs, data gaps, and quality needs
• Work with big data technologies and distributed data processing to support scalable modeling
• Partner with data engineering teams to ensure data quality, availability, and efficient data pipelines

Visualization Insights

• Create visualizations to communicateforecasts, trends, and model outputs
• Translate model results intoactionable insightsfor business and operational stakeholders

Collaboration Stakeholder Engagement

• Work closely with product managers, engineers, and business teams to define forecasting and analytics requirements
• Communicate modeling approaches, assumptions, and results clearly to technical and non-technical stakeholders

Qualifications

• 7+ years of experiencein data science, applied statistics, or machine learning
• Strong foundation instatisticsand experience applying statistical methods in production
• Proven experience withtime series analysis and probabilistic forecasting
• Hands-on experience withmachine learning modelssuch as regression, boosting, and neural networks
• Experience owningproduction data science models, including deployment and monitoring
• Experience working with large datasets and using SQL and Python for analytical and modeling workflows
• Proficiency inPythonand common DS/ML libraries (e.g., pandas, NumPy, scikit-learn, statsmodels, PyTorch/TensorFlow)
• Experience working onGoogle Cloud Platform (GCP)
• Bachelors or Masters degree inComputer Science, Statistics, Mathematics, Data Science, or a related quantitative field