Adani Group—Data Scientist - Deputy Manager

Job description

Description: About Business: Adani Group: Adani Group is a diversified organisation in India comprising 10 publicly traded companies. It has created a world-class logistics and utility infrastructure portfolio that has a pan-India presence. Adani Group is headquartered in Ahmedabad, in the state of Gujarat, India. Over the years, Adani Group has positioned itself to be the market leader in its logistics and energy businesses focusing on large-scale infrastructure development in India with O & M practices benchmarked to global standards. With four IG-rated businesses, it is the only Infrastructure Investment Grade issuer in India. Job Purpose: The Business Analyst plays a crucial role in analyzing market trends, identifying business opportunities, and leveraging AI-driven insights to drive strategic decision-making at Adani AI Labs. This role involves tracking competitive landscapes, conducting market research, and supporting AI integration to enhance business profitability, operational efficiency, and long-term growth strategies. Requirements: - Postgraduate degree in Data Science, Big Data Analytics, Statistics, Computer Science, Engineering or a related quantitative discipline. - 3–6 years of applied data science experience with at least one solution deployed to production. Preferred - Domain exposure to power and energy markets, utilities, manufacturing, cement or aviation. - Certifications in Databricks, Azure Data Engineering / AI, or cloud ML platforms. - Publications or conference presentations in applied analytics or operations research. Responsibilities: 1 Business Understanding & Solution Design - Engage business SPOCs to define problem statements, success criteria and decision workflows; convert them into analytical use cases. - Prepare Business Requirement Documents, Solution Design Documents and approach notes; obtain sign-off from business and techno-functional owners. - Define KPIs, accuracy thresholds and acceptance criteria before development begins. 3.2 Data Engineering & Governance - Source, reconcile, and validate data across internal systems, SCADA/market feeds, weather, and third-party sources. - Apply data quality controls — unique-key checks, missing-block detection, completeness checks, duplicate handling, time-zone standardization and mapping validation — before model training and reporting. - Build reproducible feature pipelines including lagged, rolling, calendar and exogenous features. 3.3 Model Development & Validation - Develop and tune machine learning and time-series models (tree-based, boosting, statistical and deep learning methods) for demand, price, sales and footfall forecasting. - Deliver computer vision and NLP solutions for compliance checks, monitoring and document/resume intelligence use cases. - Develop Generative AI and Agentic AI solutions using LLMs, RAG and tool-enabled agents to automate enterprise workflows and support intelligent decision-making. - Perform back-testing, ensemble comparison, error attribution and block-level validation; benchmark against existing baselines using MAPE, bias and unexplained variance. 3.4 Deployment & MLOps - Deploy models on Databricks and Azure with scheduled jobs, automated retraining triggers and outputs published to the Unity Catalog. - Remove manual dependencies through automation; ensure monitoring, versioning and fallback logic for production runs. - Coordinate with data engineering and IT for integration, UAT and production rollout. 3.5 Reporting, Stakeholder & Project Management - Present results, accuracy trends and recommendations to business heads and senior leadership; publish minutes of meeting and track actions. - Manage delivery through JIRA — break down epics into stories and tasks, track dependencies, risks and timelines in an Agile cadence. - Mentor junior data scientists and interns; review code, methodology and documentation.