Data Science and AIML Lead - AITDS

Data Science & AI/ML Lead (EDA Experience) Level: SM Role Overview A hands-on Data Science and AI/ML Lead responsible for owning the end-to-end model training lifecycle, starting from EDA and feature engineering through training, evaluation, and deployment readiness. The role focuses on building reproducible, production-grade ML pipelines and ensuring data and models are optimized for performance, scalability, and reliability. Key Responsibilities 1. Exploratory Data Analysis & Model Development · Translate business problems and Use cases into model-ready ML formulations. · Perform deep EDA and data profiling to understand patterns, data quality, and feature relevance · Define feature engineering strategy aligned to model performance objectives · Ensure reproducibility through dataset versioning and experiment tracking · Define pipeline strategy for continuous retraining and validation. · Train and optimize models for classification, regression, clustering, and anomaly detection, LLM/SLM Pretraining and Finetuning, etc. · Perform hyperparameter tuning and model selection for optimal performance · Drive trade-offs across accuracy, latency, cost, and interpretability 3. Scoring, Evaluation & Benchmarking · Define evaluation and scoring frameworks for Datasets and certify for AI Readiness (Model Training) · Conduct error analysis and benchmarking across datasets and model versions · Establish acceptance thresholds and quality gates for production readiness. 4. Scalable ML & MLOps Enablement · Enable ML lifecycle practices including model versioning, tracking, and monitoring · Work with cloud platforms (Azure/AWS/GCP) for scalable training and deployment · Collaborate with engineering teams to ensure production-grade integration · Optimize platform performance, reliability, and scalability. Required Capabilities / Skills / Experience · 12+ years in Data Science / Machine Learning with strong hands-on experience · Strong expertise in Python and ML/DL frameworks (scikit-learn, PyTorch, TensorFlow) · Deep experience in EDA, feature engineering, and model training pipelines · Experience building production-grade ML pipelines and evaluation frameworks · Exposure to cloud ML platforms (Azure/Vertex/SageMaker) · Experience with large-scale data processing and distributed training · Hands-on experience with classical ML algorithms (Decision Trees, Random Forest, XGBoost, Gradient Boosting etc.) · Exposure to LLM/SLM training or fine-tuning techniques (PEFT, LoRA, fine-tuning workflows) · Exposure to LLM / GenAI workflows as integration points · Familiarity with data quality, labelling, and dataset curation at scale · Strong problem-solving and system thinking skills.