Über
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. Responsibilities:
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. Requirement/Must Have:
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. Should Have:
Experience in retail, supply chain, inventory, or demand forecasting domains. Exposure to MLOps practices including CI/CD pipelines, model monitoring, and retraining workflows. Skills:
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. Qualification And Education:
Bachelor's or Master's degree in a quantitative or technical discipline.
Sprachkenntnisse
- English
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