Senior Data Scientist

Job Description – Senior Data Scientist Experience Required 8–10 years Employment Type Full-time / Contract (as applicable) Location Bangalore / Hybrid / Onsite (as applicable)

Role Overview The Senior Data Scientist will provide end-to-end analytical leadership across KPI engineering, data quality governance, and analytics consumption layers of an AWS-based enterprise data platform. The role is responsible for ensuring metric accuracy, analytical integrity, and business trust in data products, while enabling scalable, governed analytics and supporting future-ready Digital Twin and advanced analytical initiatives.

Key Responsibilities

1. KPI Engineering & Semantic Layer Governance Lead the design, standardization, and governance of enterprise KPIs across analytical platforms Define and maintain a semantic layer with consistent metric definitions, hierarchies, and calculation logic Translate complex business requirements into analytics-ready datasets aligned with the Bronze–Silver–Gold data architecture Validate and reconcile API-sourced and upstream system data to ensure semantic accuracy and metric fidelity

2. Data Quality Engineering & Analytical Validation Define and operationalize enterprise data quality frameworks, including: Completeness, accuracy, timeliness, and consistency checks Schema integrity and data drift monitoring Anomaly detection, outlier analysis, and reconciliation Partner with Data Engineering teams to embed validation logic within ETL and analytics pipelines Perform advanced root-cause analysis for KPI deviations and reporting inconsistencies

3. Advanced Analytics & Dashboard Enablement Architect and validate analytical logic for Apache Superset dashboards, including complex aggregations, derived metrics, filters, and drill-downs Ensure dashboards meet enterprise standards for accuracy, performance, usability, and scalability Design and enable role-based and persona-driven analytics views for business, operational, and leadership users

4. Data Governance, Metadata & Documentation Define and govern metric definitions, calculation methodologies, analytical standards, and business glossaries Contribute to and maintain enterprise metadata and data catalogs using AWS Glue Data Catalog and Confluence Classify datasets based on sensitivity, criticality, and regulatory requirements, supporting access governance and compliance

5. Stakeholder Enablement & Analytical Leadership Lead training and enablement programs on KPI interpretation, dashboard usage, and analytical best practices Act as a trusted advisor and bridge between business stakeholders, analytics teams, and data engineering functions Drive data literacy, adoption, and confidence in analytical outputs across the organization Required Skills & Qualifications Technical Skills Expert-level proficiency in SQL and advanced analytical data modeling Strong Python scripting experience for data analysis, validation, anomaly detection, and automation of analytical workflows Strong hands-on experience with AWS analytics services, including: Amazon S3 Amazon Athena Amazon Redshift (analytics and consumption layers) Proven experience with Apache Superset or equivalent enterprise BI platforms Deep expertise in data quality, reconciliation, and analytical validation frameworks Analytical & Statistical Skills Strong experience in KPI engineering and metric standardization Solid foundation in statistical analysis, anomaly detection, and data validation methodologies Advanced analytical reasoning and structured problem-solving abilities Professional & Leadership Skills Strong stakeholder management and executive communication skills High standards for documentation, governance, and analytical rigor Ability to influence and institutionalize analytics best practices across cross-functional teams Preferred / Nice to Have Exposure to Digital Twin, IoT, or advanced analytics platforms Experience in enterprise data platform stabilization, modernization, or transformation initiatives Background in consulting or large-scale analytics delivery environments