Senior Data Scientist

g expertise in statistics, time series analysis, probabilistic forecasting, machine learning, and Google Cloud Platform (GCP).

Responsibilities

Statistical Modeling & Forecasting

• 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

Machine Learning & Predictive Modeling

• 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

MLOps & Production Deployment

• 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

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 communicate forecasts, trends, and model outputs

• Translate model results into actionable insights for 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 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

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