Data Scientist - Deputy Manager

Role purpose

Business understanding and solution design

• Engage business SPOCs to define problem statements, success criteria, and decision workflows, and 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.

Data engineering and governance

• Source, reconcile, and validate data across internal systems, SCADA and market feeds, weather, and third-party sources.
• Apply quality controls for unique keys, missing blocks, completeness, duplicates, time zones, and mapping before model training and reporting.
• Build reproducible feature pipelines with lagged, rolling, calendar, and exogenous features.

Model development and validation

• Develop and tune machine-learning and time-series models, including 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 or resume intelligence.
• Build generative AI and agentic AI solutions using LLMs, RAG, and tool-enabled agents for enterprise workflows and decision-making.
• Back-test, compare ensembles, attribute errors, validate at block level, and benchmark against baselines using MAPE, bias, and unexplained variance.

Deployment, reporting, and project delivery

• Deploy models on Databricks and Azure with scheduled jobs, automated retraining triggers, and outputs published to Unity Catalog.
• Automate production runs and implement monitoring, versioning, and fallback logic; coordinate integration, UAT, and production rollout with data engineering and IT.
• Present results, accuracy trends, and recommendations to business heads and senior leadership; publish meeting minutes and track actions.
• Manage delivery through JIRA by breaking epics into stories and tasks and tracking 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 and at least one solution deployed to production.

Preferred qualifications

• Domain exposure to power and energy markets, utilities, manufacturing, cement, or aviation.
• Certifications in Databricks, Azure Data Engineering or AI, or cloud ML platforms.
• Publications or conference presentations in applied analytics or operations research.