You will be responsible for designing, building, and deploying ML solutions that power intelligent decision-making across supply chain and retail platforms. The primary focus will be on production ML including forecasting, optimization, recommendations, and anomaly detection on structured tabular datasets. Additionally, you will contribute to Generative AI initiatives complementing the classical ML stack. This is not a research role; we are looking for someone with experience in shipping ML systems to production and problem ownership end-to-end. **Key Responsibilities:**
Design, develop, and deploy ML models for various use cases in supply chain and retail such as forecasting, classification, regression, recommendations, and anomaly detection. - Build and maintain end-to-end ML pipelines including feature engineering, training, evaluation, inference, and monitoring using modern MLOps tooling. - Translate ambiguous business problems into well-defined ML problems with clear success metrics. - Design and analyze experiments like A/B tests and offline evaluations to quantify model and business impact. - Monitor models in production for drift and performance degradation and drive retraining cycles. - Contribute to GenAI features using prompt engineering, retrieval strategies, and evaluation frameworks. - Collaborate with product, data engineering, and domain experts to bring ideas from POC to production. **Qualifications Required:**
Python proficiency in Pandas, scikit-learn, NumPy, and production-quality scripting. - Solid understanding of Machine Learning concepts such as classification, regression, clustering, and anomaly detection. - Experience in SQL querying and working with relational databases. - Knowledge of model evaluation techniques like cross-validation, metrics, and bias/variance tradeoff. - Ability to handle real-world messy tabular data for data wrangling. - Familiarity with Big Data tools like Spark / PySpark, Snowflake / BigQuery for feature engineering at scale. - Hands-on experience with at least one cloud platform (Azure / AWS / GCP) and MLOps tools like MLflow, Airflow, or SageMaker. - Proficiency in deploying models through REST APIs, batch pipelines, or real-time inference and knowledge of Docker and basic Kubernetes. - Familiarity with DevOps practices using Git, GitHub, branching strategies, and code review discipline. - Working knowledge of LLM APIs, RAG architectures, embeddings, and vector databases. - Exposure to frameworks like LangChain, LangGraph, or LlamaIndex and understanding of GenAI evaluation metrics. **Good to Have:**
Proficiency in advanced ML techniques like XGBoost, LightGBM, or ensemble methods. - Experience with Time Series forecasting methods such as ARIMA, Prophet, or modern deep-learning approaches. - Deep understanding of MLOps tools like MLflow for experiment tracking and model lifecycle management. - Experience in delivering GenAI features like RAG or LLM-powered features to production. You will be responsible for designing, building, and deploying ML solutions that power intelligent decision-making across supply chain and retail platforms. The primary focus will be on production ML including forecasting, optimization, recommendations, and anomaly detection on structured tabular datasets. Additionally, you will contribute to Generative AI initiatives complementing the classical ML stack. This is not a research role; we are looking for someone with experience in shipping ML systems to production and problem ownership end-to-end. **Key Responsibilities:**
Design, develop, and deploy ML models for various use cases in supply chain and retail such as forecasting, classification, regression, recommendations, and anomaly detection. - Build and maintain end-to-end ML pipelines including feature engineering, training, evaluation, inference, and monitoring using modern MLOps tooling. - Translate ambiguous business problems into well-defined ML problems with clear success metrics. - Design and analyze experiments like A/B tests and offline evaluations to quantify model and business impact. - Monitor models in production for drift and performance degradation and drive retraining cycles. - Contribute to GenAI features using prompt engineering, retrieval strategies, and evaluation frameworks. - Collaborate with product, data engineering, and domain experts to bring ideas from POC to production. **Qualifications Required:**
Python proficiency in Pandas, scikit-learn, NumPy, and production-quality scripting. - Solid understanding of Machine Learning concepts such as classification, regression, clustering, and anomaly detection. - Experience in SQL querying and working with relational databases. - Knowledge of model evaluation techniques like cross-validation, metrics, and bias/variance tradeoff. - Ability to handle real-world messy tabular data for data wrangling. - Familiarity with Big Data tools like Spark / PySpark, Snowflake / BigQuery for feature engineerin
