Work Schedule
Standard (Mon-Fri)
Environmental Conditions
Office
Job Description
Job Summary
At Thermo Fisher Scientific, our mission is to enable our customers to make the world healthier, cleaner, and safer.
We are seeking a Data Analyst – Analytics Engineering & AI to support the development of modern enterprise analytics capabilities and help accelerate the evolution of digital analytics toward AI-supported insights.
Reporting to the Senior Manager, Analytics & Insights, the Data Analyst will work at the intersection of business analytics, data visualization, analytics engineering, data modeling, and AI-enabled analytics.
This role will partner with business stakeholders, senior analysts, data engineers, architects, and product teams to transform business questions into trusted datasets, analytical models, dashboards, reports, and actionable insights.
The Data Analyst will also contribute to the development of an AI-ready analytics foundation by helping organize business metrics, metadata, data relationships, definitions, and analytical datasets so they can be consistently used across dashboards, self-service analytics, conversational analytics, and emerging AI applications.
The ideal candidate is analytically curious, technically capable, comfortable working with complex data, and interested in developing skills across analytics engineering, semantic modeling, and AI-enabled analytics.
MAJOR JOB DUTIES AND RESPONSIBILITIES
- Partner with business stakeholders and senior analytics team members to understand business questions, reporting requirements, KPIs, and analytical needs.
- Analyze large and complex datasets to identify trends, patterns, opportunities, anomalies, and drivers of business performance.
- Develop recurring and ad hoc analyses supporting commercial, customer, digital, operational, and strategic initiatives.
- Build and maintain dashboards, reports, scorecards, and visualizations that provide clear and actionable insights to business stakeholders.
- Write and maintain SQL queries and data transformations used to create analytical datasets and support reporting and analysis.
- Assist in developing reusable analytical datasets and data models that improve consistency and reduce duplicated reporting logic.
- Support the development and maintenance of a trusted semantic analytics layer, including business definitions, KPIs, dimensions, measures, hierarchies, and relationships.
- Work with senior analysts and business stakeholders to document and validate metric definitions and ensure consistent interpretation across analytical products.
- Perform data profiling, validation, reconciliation, and quality checks to identify inconsistencies, missing data, or unexpected results.
- Investigate data-quality and reporting issues and partner with analytics engineering and data engineering teams to identify root causes.
- Support the creation and maintenance of documentation covering data sources, transformations, metric calculations, business rules, assumptions, and analytical logic.
- Assist with ETL/ELT and analytics engineering activities, including data transformations, testing, validation, and maintenance of curated analytical datasets.
- Use Python or similar analytical technologies for data preparation, automation, exploratory analysis, validation, and analytical workflows as appropriate.
- Support customer, product, digital, marketing, commercial, and operational analytics initiatives through data exploration and performance measurement.
- Assist with experimentation, segmentation, funnel analysis, customer journey analysis, KPI tracking, forecasting, and other analytical methodologies as needed.
- Work with senior team members to identify opportunities to automate manual reports, repetitive analysis, and data-preparation processes.
- Support the transition from traditional reporting toward more self-service, proactive, and AI-enabled analytical experiences.
- Participate in the development and testing of conversational analytics, natural-language querying, AI-generated insights, semantic search, and other emerging analytical capabilities.
- Validate AI-generated analytical outputs by comparing responses against trusted datasets, established metrics, source systems, and documented business rules.
- Help organize metadata, business definitions, and semantic context so AI-enabled analytics solutions can more accurately interpret enterprise data.
- Collaborate with data engineering, architecture, security, product, and governance teams to ensure analytical solutions follow organizational standards.
- Participate in peer reviews, testing, documentation, and continuous improvement of analytics products and processes.
- Communicate analytical findings clearly to both technical and non-technical stakeholders using effective visualizations, summaries, and recommendations.
- Continuously develop knowledge of modern analytics, data engineering, semantic technologies, cloud data platforms, generative AI, and emerging analytical practices.
QUALIFICATIONS (Education/Training, Experience and Certifications)
- Bachelor's degree in Computer Science, Data Science, Engineering, Information Systems, Business Analytics, Statistics, Mathematics, Economics, or a related quantitative discipline.
- Equivalent combination of education and relevant professional experience may be considered.
- Advanced degree or relevant professional certifications are beneficial but not required.
- 2–5 years of experience in data analytics, business intelligence, analytics engineering, data engineering, data science, or a related discipline.
- Experience working with structured datasets and translating business questions into analytical outputs.
- Experience developing dashboards, reports, analyses, or analytical datasets in a business environment.
- Experience working with relational databases, cloud data warehouses, or enterprise analytical platforms.
- Exposure to data modeling, dimensional modeling, semantic models, metrics layers, or curated analytical datasets is preferred.
- Experience with cloud-based data platforms such as Snowflake, Teradata, Hadoop, AWS, Azure, Google Cloud Platform, or similar technologies is beneficial.
- Experience with digital analytics technologies such as Google Analytics or Adobe Analytics is beneficial.
- Exposure to enterprise systems such as SAP, ERP, CRM, digital commerce, marketing, customer, or operational platforms is preferred.
- Exposure to generative AI, conversational analytics, natural-language querying, semantic search, machine learning, or AI-enabled analytics is beneficial
TECHNICAL SKILLS
- Strong SQL skills, including joins, aggregations, subqueries, common table expressions, window functions, and analytical querying.
- Working knowledge of Python or another analytical programming language for data manipulation, automation, analysis, or validation.
- Experience with visualization and business intelligence technologies such as Tableau, Power BI, or similar tools.
- Understanding of data structures, relational databases, analytical datasets, and basic data modeling concepts.
- Familiarity with ETL/ELT concepts and modern data-transformation workflows.
- Understanding of data-quality principles, including validation, reconciliation, completeness, consistency, and accuracy.
- Familiarity with version control, documentation, testing, or collaborative software/data development practices is beneficial.
- Basic understanding of cloud data architectures and modern data warehouse technologies.
- Interest in or exposure to generative AI, large language models, semantic search, embeddings, knowledge models, or AI agents is preferred.
- Ability to understand how business definitions, metadata, data relationships, and metric calculations affect analytics and AI-generated insights.
ANALYTICS & AI SEMANTIC ENGINEERING CAPABILITIES
The Data Analyst will have the opportunity to develop capabilities beyond traditional reporting and contribute to the semantic and analytical foundation supporting next-generation analytics.
Key responsibilities may include:
- Supporting the definition and maintenance of reusable business metrics and KPIs.
- Helping document business terminology and map business concepts to enterprise data.
- Assisting in defining relationships between customers, products, channels, transactions, campaigns, digital interactions, and other business entities.
- Building analytical datasets that can be reused across dashboards, reports, analyses, and AI-enabled applications.
- Helping identify differences between business terminology and source-system definitions.
- Contributing metadata, definitions, descriptions, and contextual information that improve self-service and AI-enabled analytics.
- Testing natural-language questions against analytical datasets and identifying cases where AI-generated responses are inaccurate or ambiguous.
- Validating AI-generated analytical responses against trusted data and established business definitions.
- Supporting data and semantic quality controls that improve accuracy, consistency, explainability, and trust.
- Developing an understanding of how governed enterprise data can support AI agents and conversational analytical experiences.
CORE COMPETENCIES
- Strong analytical and problem-solving skills.
- Curiosity and willingness to investigate unfamiliar data and business problems.
- Ability to translate data into clear observations and business insights.
- Strong attention to detail and commitment to analytical accuracy.
- Ability to communicate findings effectively to technical and non-technical audiences.
- Ability to work collaboratively across analytics, engineering, product, technology, and business teams.
- Ability to manage multiple assignments and priorities in a fast-paced environment.
- Willingness to ask questions, challenge assumptions constructively, and seek deeper understanding of business problems.
- Strong documentation and organizational skills.
- Commitment to continuous learning and development across analytics, data engineering, and AI technologies.