Job

Senior Data Scientist – AI, Data Engineering & Intelligent Automation

Bosch Group India

Deadline: Not specified

Short Summary

Senior Data Scientist – AI, Data Engineering & Intelligent Automation at Bosch Group. About the employer 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…

Key Details

Position / opportunity: Senior Data Scientist – AI, Data Engineering & Intelligent Automation
Organization: Bosch Group
Country / coverage: India
Location: bangalore, , India
Work arrangement: On-site
Opportunity type: Jobs
Sector: Information Technology
Compensation: Not specified
Application deadline: Not specified by source

Description

About the employer

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.

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.

Experience: 6-8 years of experience in Data Science, Data Engineering, AI, or Analytics domains. Proven experience delivering end-to-end solutions from data acquisit

Qualifications

B.E

Additional information

6 to 8 years

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

Requirements / Eligibility

How To Apply

Select Apply now to complete the application for this position. Submit the documents requested in the application form.