Job Details

Job Description:

• The Senior Data Scientist will design, build, and deploy advanced forecasting and analytics solutions with a strong focus on time-series modeling, cloud-based data platforms, and supply chain demand forecasting.
• This role partners closely with business and engineering teams to deliver scalable, high-impact data science solutions.

• Build, train, and deploy time-series forecasting models using XGBoost, Random Forest, linear regression-based models, ARIMA, SARIMA, Holt-Winters, and Prophet.
• Operationalize end-to-end model pipelines using cloud platforms such as Google Cloud Platform, AWS, or Azure.
• Perform feature engineering, hyperparameter tuning, model validation, and continuous improvement of forecasting pipelines.
• Develop scalable data processing workflows using Python, PySpark, and SQL.
• Work with large datasets in cloud-native analytics and modeling environments.
• Collaborate with data engineering teams to ensure reliable data pipelines and high-quality training data.
• Design and implement anomaly detection systems to identify volume, pattern, and trend deviations.
• Build automated alerting frameworks for early detection of data or forecast issues.
• Perform root cause analysis on anomalies and recommend long-term corrective actions.
• Develop and maintain accuracy measurement frameworks including MAPE, RMSE, MAE, WAPE, and bias metrics.
• Continuously optimize model performance based on accuracy evaluations.
• Communicate model performance, accuracy trends, and insights to technical and business stakeholders.
• pply domain expertise in retail supply chain, inventory planning, replenishment, and demand forecasting.
• Partner with business teams to translate forecasting requirements into data science solutions.

• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
• Minimum of 5 years of experience as a Data Scientist with hands-on model development.
• Strong programming skills in Python including PySpark, Pandas, NumPy, SciPy, and scikit-learn.
• Hands-on experience with time-series forecasting, regression models, and ensemble techniques.
• Strong SQL skills with experience working on large-scale datasets.
• Experience working with Google Cloud Platform or similar cloud environments.
• bility to diagnose data issues, perform anomaly analysis, and build automated alerting solutions.

• Experience in retail, supply chain, inventory, or demand forecasting domains.
• Exposure to MLOps practices including CI/CD pipelines, model monitoring, and retraining workflows.

• Time-series forecasting and statistical modeling.
• Cloud-native data science and analytics.
• Data anomaly detection and alerting systems.
• Strong analytical, problem-solving, and communication skills.

• Bachelor's or Master's degree in a quantitative or technical discipline.