Deputy Manager - Data Scientist

adani capital pvt ltd; Ahmedabad District; Hybrid; INR 1,200,000 - 1,800,000; Full time

Job summary

Adani Capital Pvt Ltd. is seeking a Data Scientist at Deputy Manager level to translate business problems into analytical use cases and lead end-to-end model development and deployment.

You will work with cross-functional teams to design KPIs, validate data, and deliver production-ready solutions, including NLP/ computer vision for compliance and insights.

Qualifications

• Postgraduate degree in Data Science or related quantitative discipline.
• 3–6 years of applied data science experience with at least one solution deployed to production.
• Preferred 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

• Engage business SPOCs to define problem statements, success criteria and decision workflows.
• Source, reconcile, and validate data across internal systems, SCADA/market feeds, weather, and third-party sources; apply data quality controls.
• Develop and tune machine learning and time-series models for forecast use cases; deliver NLP and CV solutions for checks and intelligence.
• Deploy models on Databricks and Azure with automated retraining and monitoring; coordinate with IT for deployment.
• Present results and recommendations to leadership; manage delivery through agile tools and mentor junior staff.

Skills

Stakeholder management

KPIs definition

Model development

MLOps

Databricks

Azure

NLP/Computer Vision

Education

Postgraduate degree in Data Science or related

Tools

Databricks

Azure

Unity Catalog

JIRA

LLM tools

Job description

Data Scientist - Deputy Manager 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.

Qualifications 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.