Thermo Fisher Scientific
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Bengaluru · On-site · 6-9 Yrs · 4 days ago
Biotech & Pharmaceuticals · 10000+ Employees · Waltham, US
As the world leader in serving science, we empower our people to advance innovative technologies, develop meaningful solutions, and build rewarding careers. With annual revenue of approximately $40 billion and the largest investment in R&D in the industry, we give our 100,000 extraordinary minds the resources and opportunities to make significant contributions to the world. Explore the professional possibilities at Thermo Fisher Scientific, and join us.
Hiring: Engineering
What employees say
+ Great technology stack and learning opportunities. Good exposure to modern automation tools and frameworks.
− I have experienced a significant amount of workplace politics during my 8 months here. At times, I have felt a lack of inclusiveness and found it challenging to feel like a local t
More reviews on the company profile.
Interview experience · Software Automation Tester
Before interview just go through the online questions. Interviewer read out the questions from another screen ask the same. But it's a good one to know some of the topics which which not required in your daily automation
Average difficulty · Bengaluru · 157 reports
More interview insightsJob description
Overview
Environmental Conditions
Qualifications
- Bachelor's degree in Computer Science, Data Science, Engineering, Information Systems, Business Analytics, Statistics, Mathematics, Economics, or a related quantitative discipline.
- Master's degree in a quantitative, technical, or business discipline preferred.
- 6+ years of experience in data analytics, business intelligence, analytics engineering, data engineering, data science, or a related discipline.
- Demonstrated experience translating ambiguous or complex business problems into structured analytical solutions.
- Strong experience working with enterprise-scale data environments and large, complex datasets.
- Advanced SQL skills, including complex transformations, joins, window functions, optimization, reconciliation, and analytical querying.
- Proficiency with Python for analytics, data manipulation, automation, validation, or analytical application development.
- Strong understanding of data modeling, including dimensional modeling, fact/dimension structures, analytical datasets, and reusable business entities.
- Understanding of modern ETL/ELT and analytics engineering practices, including transformation pipelines, testing, documentation, version control, and deployment.
- Understanding of data quality, data lineage, metadata management, governance, and master/reference data concepts.
- Knowledge of APIs, structured and semi-structured data, cloud data architectures, and modern data integration patterns.
- Familiarity with generative AI, large language models, retrieval-based architectures, semantic search, embeddings, knowledge models, or AI agents is preferred.
- Understanding of how metadata, business terminology, semantic relationships, metric definitions, and governed data influence the accuracy and reliability of AI-generated analytical responses.
- Defining reusable enterprise business metrics and KPI logic.
- Modeling relationships between customers, products, channels, transactions, campaigns, digital interactions, and other important business entities.
- Creating analytical models that can be consumed consistently by humans, BI platforms, APIs, and AI applications.
- Translating business terminology into structured metadata and machine-understandable definitions.
- Identifying and resolving inconsistencies between source-system terminology and enterprise business definitions.
- Designing analytical context that improves natural-language querying and AI interpretation of enterprise data.
- Testing AI-generated analytical answers against governed data and established metric definitions.
- Helping establish guardrails that ensure AI-enabled analytics respect data access, governance, privacy, security, and business rules.
- Balancing emerging AI capabilities with accuracy, explainability, reproducibility, and trusted enterprise data.
- Strong analytical and structured problem-solving skills.
- Ability to move fluidly between business problems and technical implementation.
- Strong curiosity and ability to uncover the business meaning behind data.
- Ability to communicate complex analytical and technical concepts clearly to both technical and non-technical audiences.
- Strong stakeholder-management and consulting skills.
- Ability to independently manage multiple priorities and analytical initiatives.
- Strong attention to data quality, analytical accuracy, and business context.
- Ability to challenge assumptions constructively and use data to influence decisions.
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Job application FAQ
Last updated 3 October 2026
- How do I apply for Senior Data Analyst – Analytics Engineering & AI at Thermo Fisher Scientific?
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- Is Senior Data Analyst – Analytics Engineering & AI remote or on-site in Bengaluru, Karnataka?
- This listing is marked on_site with location Bengaluru, Karnataka. Confirm details on the job description before applying.
- What skills matter for this Senior Data Analyst – Analytics Engineering & AI role at Thermo Fisher Scientific?
- This is a senior Senior Data Analyst – Analytics Engineering & AI role. Key skills include skills listed in the job description. Feedback AI highlights matched vs missing skills on your profile after signup.