Data Scientist - Deputy Manager

About the role

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.