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We are looking for a highly skilled Senior Data Scientist with deep expertise in AI/ML techniques for Time Series Forecasting. The candidate will design, develop, and deploy advanced predictive models for large-scale forecasting use cases such as retail item‑location level demand forecasting. This role will contribute to end-to-end model development, from research and experimentation to production deployment, while collaborating with cross-functional teams including data science, engineering, and product. The position also offers exposure to a broad spectrum of data science domains including predictive modeling, personalization, recommendation systems, NLP, search ranking, optimization, and supply chain analytics.
Job Responsibility:
Develop, maintain, and optimize Machine Learning (ML) forecasting models to improve accuracy and scalability
Work closely with data scientists, data engineers, and business teams to translate requirements into scalable and maintainable solutions
Research and evaluate state-of-the-art ML and deep learning techniques relevant to forecasting and optimization
Build end-to-end ML workflows including feature engineering, model training, validation, deployment, and monitoring
Follow coding standards, version control best practices, and documentation guidelines
Contribute to the design and development of scalable data pipelines and ML infrastructure
Monitor model performance and implement strategies for drift detection, retraining, and continuous improvement
Requirements:
Bachelor's degree in computer science, Data Science, Mathematics, or a related technical field
5–8 years of experience in Data Science OR 3+ years of direct experience in developing forecasting solutions
Strong knowledge of forecasting techniques including: Time Series: ARIMA, SARIMA, Prophet
Machine Learning: GLM, GBDT, XGBoost
Hierarchical Models: Top‑Down, Bottom‑Up
Deep Learning: Seq2Seq, Transformers, LSTM/GRU
Ensemble methods
Hands-on experience with end-to-end ML development, from data preparation to productionization
Experience with ML platforms/tools such as MLflow, Kubeflow, Databricks, or equivalents
Strong background in Big Data tools & distributed computing: Spark, Hadoop ecosystem, Airflow, Druid, Superset
Expertise in Python, Spark, and SQL
Experience with Git, Bitbucket, CI/CD, and version control practices
Familiarity with ML monitoring and model performance evaluation tools
Nice to have:
Experience optimizing, debugging, and fine-tuning ML models in distributed systems
Understanding of forecasting at scale for retail, supply chain, demand planning, or capacity planning domains
Exposure to optimization techniques and mathematical programming (e.g., linear programming, network flows)
Knowledge of NLP, recommendation models, ranking algorithms, or search optimization
Experience with cloud platforms (Azure, AWS, GCP) for ML deployment
Excellent analytical, problem‑solving, and communication skills