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,…