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

• 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
• 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.
• 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.