Senior Data Scientist

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

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

Responsibilities:

• Build and deploy statistical and probabilistic forecasting models for demand, capacity, and trend analysis
• Apply time series methods including regression-based models, ARIMA/SARIMA, and state-space models
• Define modeling assumptions, forecast uncertainty, and evaluation methods
• Build and deploy machine learning models for forecasting and prediction, including regression, tree-based models, gradient boosting, and neural networks
• Select statistical or ML approaches based on accuracy, interpretability, robustness, and operational needs
• Develop features and run experiments to improve model performance
• Own the model lifecycle, including development, backtesting, deployment, monitoring, and retraining
• Implement model monitoring, performance tracking, and data drift detection
• Ensure models are versioned, reproducible, and production-ready
• 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
• Create visualizations to communicate forecasts, trends, and model outputs
• Translate model results into actionable insights for business and operational stakeholders
• 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:

• 5+ years of experience in data science, applied statistics, or machine learning
• Strong foundation in statistics and experience applying statistical methods in production
• Proven experience with time series analysis and probabilistic forecasting
• Hands-on experience with machine learning models such as regression, boosting, and neural networks
• Experience owning production data science models, including deployment and monitoring
• Experience working with large datasets and using SQL and Python for analytical and modeling workflows
• Proficiency in Python and common DS/ML libraries (e.g., pandas, NumPy, scikit-learn, statsmodels, PyTorch/TensorFlow)
• Experience working on Google Cloud Platform (GCP)
• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field