Senior Data Scientist – AI, Data Engineering & Intelligent Automation

Job Description

Key Responsibilities:

• Data Analytics/Pyspark/Azure data bricks
• Pipeline Design & Development: Build, monitor, and maintain robust, scalable batch and real-time ETL/ELT pipelines using PySpark, SQL, Delta Lake, and Auto Loader.
• Data Governance & Security: Enforce data access policies, row/column-level security, and lineage tracking using Databricks Unity Catalog to protect PII and maintain compliance
• Orchestration & Workflow Automation: Schedule and orchestrate multi-task jobs using Databricks Workflows (Lakeflow Jobs) or external schedulers (e.g., Apache Airflow, Azure Data Factory)
• Data Quality Assurance: Define and integrate automated data quality rules, validation checks, and testing frameworks (e.g., Great Expectations, Delta Live Tables expectations) directly into pipelines
• Big Data handling :Design and implement scalable data ingestion, transformation, and processing pipelines.
• Automate data collection, validation, and integration workflows.
• Ensure data quality, reliability, and governance across the data ecosystem.
• Interface with diverse enterprise data sources including databases, APIs, cloud platforms, files, streaming systems, and third-party applications.
• Analyze structured and unstructured data to derive actionable insights and recommendations.
• Design experiments, evaluate data performance, and continuously improve solution effectiveness.Apply advanced analytics techniques to support business decision-making.
• Data Visualization & Business Insights. Build intuitive dashboards and visualizations to communicate insights effectively.
• Translate technical findings into business-friendly recommendations.
• Support leadership teams with data-driven decision-making.AI, GenAI & Agentic Solutions.
• Design and develop AI-powered applications using modern Large Language Models (LLMs).
• Build Agentic AI workflows and intelligent assistants for enterprise use cases.
• Implement Model Context Protocol (MCP)-based integrations and orchestration frameworks.
• Develop Retrieval-Augmented Generation (RAG) and enterprise knowledge solutions.
• Deployment, Cloud & Infrastructure Deploy AI and analytics solutions across cloud and on-premises environments.
• Design scalable solution architectures on Azure, AWS, GCP, or hybrid platforms.
• Containerize applications using Docker.
• Collaborate with infrastructure and DevOps teams to ensure production-grade deployments.
• Stakeholder Management & Technical Leadership. Work closely with business stakeholders to understand requirements and define solution roadmaps. Lead technical discussions and provide architectural guidance.
• Present complex technical concepts to both technical and non-technical audiences.
• Mentor junior team members and promote engineering best practices.

Required Technical Skills:

• Data Science & Analytics, Machine Learning, Statistical Modeling, Predictive Analytics, Data Mining, Feature Engineering Model Evaluation and Optimization.
• Programming & Development: Python (Mandatory), SQL (Mandatory)
• Strong scripting and automation skills. REST APIs and system integrations, Version control systems (Git)Data Engineering, ETL/ELT Development, Data Pipeline Design, Data Warehousing Concepts, Workflow Automation, Structured and Unstructured Data Processing, Data Visualization, Power BI, Tableau, Looker, or equivalent platforms, Dashboard Development, Data Storytelling, Cloud & Infrastructure, Azure, AWS, or GCP, Docker, Kubernetes, CI/CD Concepts, Solution Deployment and Monitoring AI & Generative AI, Large Language Models (LLMs)Claude, OpenAI, and related AI platforms, Agentic AI Frameworks , Model Context Protocol (MCP), Prompt Engineering, RAG Architectures, AI Application Deployment.

Qualifications:

• Bachelor's Degree (BE/BTech) in Computer Science, Information Technology, Data Science, Engineering, Mathematics, Statistics, or a related field.
• Master's Degree is an added advantage.
• 8+ years of experience in Data Science, Data Engineering, AI, or Analytics domains. Proven experience delivering end-to-end solutions from data acquisition

B.E

6 to 8 years