{"id":1748860,"url":"https://alion.io/job/bosch-senior-data-scientist-ai-data-engineering-intelligent-automation","title":"Senior Data Scientist – AI, Data Engineering & Intelligent Automation","company":{"id":132,"name":"Bosch","domain":"bosch.com","url":"https://alion.io/company/bosch","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":{"grade":"B","score":75,"open_postings":1002,"ghost_share":0,"stale_share":0.994,"repost_share":0,"time_to_fill_p50_days":30,"computed_at":"2026-10-21T05:46:15Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":21000,"max_usd":42000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":71},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Airflow","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Docker","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"Great Expectations","optional":false},{"name":"Kubernetes","optional":false},{"name":"Looker","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"OpenAI","optional":false},{"name":"Power BI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false},{"name":"Unstructured.io","optional":false},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-10-02T15:51:35Z","employer_posted_date":"2026-10-15","last_verified_at":"2026-10-21T11:56:00Z","board_verified":true,"closed_at":null,"days_open":18,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":18},"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.\n Job Description\nKey Responsibilities:\nData Analytics/Pyspark/Azure data bricks\nPipeline Design & Development: Build, monitor, and maintain robust, scalable batch and real-time ETL/ELT pipelines using PySpark, SQL, Delta Lake, and Auto Loader.\nData Governance & Security: Enforce data access policies, row/column-level security, and lineage tracking using Databricks Unity Catalog to protect PII and maintain compliance\nOrchestration & Workflow Automation: Schedule and orchestrate multi-task jobs using Databricks Workflows (Lakeflow Jobs) or external schedulers (e.g., Apache Airflow, Azure Data Factory)\nData 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\nBig Data handling :Design and implement scalable data ingestion, transformation, and processing pipelines.\nAutomate data collection, validation, and integration workflows.\nEnsure data quality, reliability, and governance across the data ecosystem.\nInterface with diverse enterprise data sources including databases, APIs, cloud platforms, files, streaming systems, and third-party applications.\nAnalyze structured and unstructured data to derive actionable insights and recommendations.\nDesign experiments, evaluate data performance, and continuously improve solution effectiveness.Apply advanced analytics techniques to support business decision-making.\nData Visualization & Business Insights. Build intuitive dashboards and visualizations to communicate insights effectively.\nTranslate technical findings into business-friendly recommendations.\nSupport leadership teams with data-driven decision-making.AI, GenAI & Agentic Solutions.\nDesign and develop AI-powered applications using modern Large Language Models (LLMs).\nBuild Agentic AI workflows and intelligent assistants for enterprise use cases.\nImplement Model Context Protocol (MCP)-based integrations and orchestration frameworks.\nDevelop Retrieval-Augmented Generation (RAG) and enterprise knowledge solutions.\nDeployment, Cloud & Infrastructure Deploy AI and analytics solutions across cloud and on-premises environments.\nDesign scalable solution architectures on Azure, AWS, GCP, or hybrid platforms.\nContainerize applications using Docker.\nCollaborate with infrastructure and DevOps teams to ensure production-grade deployments.\nStakeholder Management & Technical Leadership. Work closely with business stakeholders to understand requirements and define solution roadmaps. Lead technical discussions and provide architectural guidance.\nPresent complex technical concepts to both technical and non-technical audiences.\nMentor junior team members and promote engineering best practices. \nRequired Technical Skills:\nData Science & Analytics, Machine Learning, Statistical Modeling, Predictive Analytics, Data Mining, Feature Engineering Model Evaluation and Optimization.\nProgramming & Development: Python (Mandatory), SQL (Mandatory)\nStrong 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.\nQualifications:\nBachelor's Degree (BE/BTech) in Computer Science, Information Technology, Data Science, Engineering, Mathematics, Statistics, or a related field.\nMaster's Degree is an added advantage.\n8+ years of experience in Data Science, Data Engineering, AI, or Analytics domains. 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