Senior Data Scientist – AI, Data Engineering & Intelligent Automation

About the job

Company Description Bosch Global Software Technologies Private Limited is a 100% owned subsidiary of Robert Bosch GmbH, one of the world's leading global supplier of technology and services, offering end-to-end Engineering, IT and Business Solutions. With over 27,000+ associates, it’s the largest software development center of Bosch, outside Germany, indicating that it is the Technology Powerhouse of Bosch in India with a global footprint and presence in the US, Europe and the Asia Pacific region.

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 InsightsBuild 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

Qualifications B.E

Additional Information 6 to 8 years