Job Description
Work Schedule
Standard (Mon-Fri)
Environmental Conditions
Office
Join our team at Thermo Fisher Scientific as a Data Scientist III within the Data Science Center of Excellence. In this role, you will contribute to improving business efficiency and decision-making across the enterprise by developing and deploying data-driven insights and solutions.
Working at the intersection of business and technology, you will partner with cross-functional teams to understand business needs, perform advanced analytics, and deliver scalable machine learning and AI solutions. You will apply techniques such as machine learning, natural language processing, and statistical modeling to solve complex commercial and operational challenges, including generating actionable recommendations delivered through platforms like Salesforce.
This role offers the opportunity to work with modern data platforms and cloud technologies (Databricks, AWS), contribute to production-grade data science solutions, and drive measurable business impact. The ideal candidate combines strong technical expertise, solid business acumen, and the ability to translate analytical outputs into clear, actionable insights.
KEY RESPONSIBILITIES
Commercial Analytics & Solution Development
- Design, develop, and validate predictive models, optimization algorithms, and recommendation systems
- Translate business problems into analytical approaches that drive measurable outcomes (e.g., revenue growth, conversion rates, operational efficiency)
- Develop scalable, interpretable, and actionable insights and recommendations for business stakeholders
- Partner with Sales, Marketing, Commercial Operations, Product, and IT to refine use cases and support adoption
- Apply statistical analysis, experimental design, and hypothesis testing to evaluate performance and impact
Technical Execution & Data Engineering Collaboration
- Build, train, and deploy machine learning models using Python-based tools (e.g., scikit-learn)
- Work with large-scale data using cloud platforms and distributed computing tools (Databricks, Spark, AWS)
- Collaborate with Data Engineers to source, transform, and manage data across complex data environments
- Contribute to the development and maintenance of data pipelines and ETL processes
- Use SQL for data exploration, transformation, and validation
- Support model operationalization and integration into business systems, including Salesforce workflows
Visualization, Communication & Continuous Improvement
- Develop dashboards and visualizations using tools such as Power BI or Tableau
- Communicate findings and recommendations clearly to both technical and non-technical audiences
- Contribute to best practices in experimentation, documentation, version control, and code quality
- Monitor model performance, adoption, and business impact, and iterate on solutions as needed
- Stay current with emerging technologies, tools, and industry trends in data science and AI
REQUIREMENTS:
Minimum Qualifications:
- Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, or related quantitative field
- 5+ years of experience in data science, machine learning, or a related field
- Strong programming skills in Python (experience with R is a plus) and SQL
- Experience developing and deploying machine learning models in production environments
- Hands-on experience with cloud platforms (AWS preferred) and big data tools (Spark, Databricks)
- Experience with data visualization tools (Power BI, Tableau)
- Familiarity with ETL processes, data pipelines, and version control systems (Git)
- Solid understanding of statistical analysis, experimental design, and hypothesis testing
- Strong problem-solving skills and ability to translate business needs into analytical solutions
- Effective communication skills with the ability to present technical concepts to diverse audiences
Preferred Qualifications:
- Master’s or PhD in Data Science, Computer Science, Statistics, Mathematics, or related quantitative field and 3+ years of experience in data science or a related field
- Experience in B2B, life sciences, healthcare, or related industries
- Experience building recommendation systems or sales optimization models
- Familiarity with Salesforce data models and CRM workflows
- Experience with natural language processing (NLP) techniques
- Understanding of MLOps, DevOps practices, and model lifecycle management
- Experience measuring model performance, user adoption, and business impact
