Department : Market Innovation Level - Senior position Expertise in supervised, unsupervised, machine learning, deep learning, reinforcement learning, statistics techniques. Proficiency in Python, PyTorch, TensorFlow. Good to have knowledge of Bayesian inference, probability distribution, hypothesis testing, A/B testing, and time series forecasting. Hands-on experience with feature engineering and hyperparameter tuning. Experience with MLflow, Weights & Biases, DVC (Data Version Control). Ability to track model performance across multiple experiments and datasets. End-to-end ML lifecycle management from Data ingestion, preprocessing, feature engineering, model training, deployment, and monitoring. Expertise in CI/CD for ML, containerization (Docker, Kubernetes), and orchestration (Kubeflow). Good to have experience in automating data labelling and feature stores (Feast). Good to have data processing experience in Spark, Flink, Druid, Nifi. Good to have real-time data streaming in Kafka and other messaging systems Designing and optimizing data pipelines for structured data. Good to have hands-on experience with Pyspark. Strong SQL skills for data extraction, transformation, and query optimization. Atleast one database experience such as NOSQL database. Implementing parallel and distributed ML techniques for large-scale systems. Good to have knowledge of model explainability (SHAP, LIME), bias mitigation, and adversarial robustness. Experience in model drift monitoring. Good to have CUDA for GPU acceleration. Good to have choosing between CPU/GPU for different ML workloads (batch inference on CPU, training on GPU). Good to have scaling deep learning models on multi-GPU. Strong presentation skills, including data storytelling, visualization (Matplotlib, Seaborn, Superset), and report writing. Experience in mentoring data scientists, research associates, and data engineers. Contributions to research papers, patents, or open-source projects.
