- You will own development and implementation of end-to-end life cycle of machine learning solutions from data modelling, feature engineering, ML solution structuring to MLOps process implementation of automated model serving
- You will play the role of a technology architect to design efficient MLOps process for large scale model deployments and high frequency servings using Azure data and ML services
- You will provide thought leadership to solution architects and project managers to come up with effective solution architecture for client’s problems
- You will be building heuristics, Operations research techniques based (like linear programming and discrete optimization) solutions to solve optimization problems in supply chain space
- You will also lead the data engineering work activities within the projects to create required data models with features stores for ML implementations and post processing activities to make the outputs consumable for business use cases
Data Science and Machine Learning Skills
- Understanding of statistical methods (e.g., regression, hypothesis testing) and optimization techniques like linear programming and mixed-integer programming for supply chain problems
- Proficiency in methods like ARIMA, SARIMAX, Prophet, or advanced techniques using neural networks (e.g., LSTMs, Temporal Fusion Transformer)
- Familiarity with supervised and unsupervised learning for classification (e.g., demand segmentation) and clustering (e.g., supplier categorization)
- Knowledge of CI/CD pipelines for ML, including retraining, deployment, and monitoring models using Azure DevOps or GitHub Actions
- Seasoned expertise in demand forecasting using ML. Understanding the nuances of intermittent, erratic and lumpy demand patterns and how to solve them using ML techniques
- Knowledge of EOQ, reorder point models, safety stock modelling and inventory simulation techniques would be a plus
- Expertise in setting up end to end MLOps processes - model training, deployment, and tracking experiments
- Expertise in creating data pipelines for ETL processes and connecting supply chain data sources Proficiency in integrating ERP data from systems like SAP into Azure via connectors or APIs
- Deploying scalable ML models as APIs using AKS (Kubernetes)
- Expertise in handling large-scale supply chain datasets using Spark, Databricks, or Azure Synapse
- Advanced query skills in Azure SQL Database or Cosmos DB for real-time analytics
- Advanced proficiency in Python for ML modelling, data analysis, and libraries like Scikit-learn, PyTorch, TensorFlow
- Version control and automating deployments using Azure DevOps or GitHub Actions
- Ability to think through automation, pipeline design and other MLOps processes
- Conceptual and pragmatic knowledge of the concepts of data modelling, feature engineering, fine tuning machine learning models, statistical model validation
- Engineering degree in computer science, informatics, data analytics and other relevant branches
- Affinity for new technologies and a drive for independent learning
- Affinity for an open feedback culture with flat hierarchies
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