Work Schedule
Standard (Mon-Fri)
Environmental Conditions
Office
Job Description
The Thermo Fisher Scientific Corporate Digital team is looking for a technically skilled, agile, and solutions-oriented AI Data Solutions Analyst with a strong background in data engineering and analytics to provide actionable insights powered by generative AI. As an integral part of the Corporate Customer Marketing team, you will be responsible for building and maintaining scalable data solutions, enabling advanced analytics across multiple structured and unstructured data sources.
This role will support senior leadership by providing deep technical expertise (Databricks, Python, PySpark, SQL, Excel) that frees capacity for executive presentations, stakeholder requirements, and high-level strategy planning. You will thrive in an environment where projects are high visibility, timelines are tight, and requirements evolve quickly.
You must have experience with modern data tools, process documentation, and technical problem solving.
With an analytical, inventive approach, you will leverage large and complex datasets to deliver insights that directly impact strategic decisions. By collaborating across business and technical teams, you will ensure our AI data solutions are accurate, scalable, and aligned with business needs.
When you’re part of the team at Thermo Fisher Scientific, you’ll do important work, like helping customers in finding cures for cancer, protecting the environment, or making sure our food is safe. Your work will have real-world impact, and you will be supported in achieving your career goals.
Primary Responsibilities:
- Work with product managers, data engineers, and business partners to build and maintain scalable AI-driven data solutions using Databricks, Python, PySpark, and SQL.
- Support generative AI applications by preparing, cleaning, and integrating datasets from multiple enterprise and third-party sources.
- Develop dashboards, models, and automated reporting to transform complex data into clear, actionable insights.
- Provide technical problem-solving expertise to troubleshoot data pipelines, optimize performance, and ensure timely delivery of outputs.
- Document and standardize processes through clear technical and procedural documentation to support repeatability and scalability.
- Collaborate with stakeholders to translate evolving business requirements into agile technical implementations.
- Support executive presentations and high-level strategy by delivering reliable, accurate, and well-documented technical outputs.
- Remain adaptable to fast-paced, high-visibility projects with changing priorities and requirements.
Minimum Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or related technical field.
- 2+ years of hands-on experience with Python, PySpark, SQL, and Databricks
- Strong experience with data preparation, cleaning, and integration for analytics and machine learning workflows.
- Familiarity with cloud-based environments (Azure, AWS, or GCP) and modern data architectures.
- Demonstrated ability to document and standardize processes, including writing technical and procedural documentation.
- Proven ability to manage multiple projects simultaneously in a fast-paced environment.
- Strong communication skills with the ability to translate technical outputs into business-relevant insights.
- Results-oriented, agile, and adaptable, with a strong sense of accountability and attention to detail.
Preferred Qualifications:
- Experience supporting AI/ML model development or integration into business workflows.
- Design and optimize relational and graph database architectures to support advanced content mapping and analytics, leveraging centrality metrics (e.g., degree, betweenness, eigenvector) to enable semantic clustering, relationship modeling, and highly relevant retrieval in AI/LLM-driven systems
- Familiarity with marketing and sales data, and how it can be leveraged to improve customer engagement and pipeline performance.
- Strong collaboration skills with cross-functional teams, including product management, technical, and business stakeholders.
