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# Sr. Manager, Data Engineering & Applied Data Science

- **Company:** Thermofisher Scientific
- **Location:** Pittsburgh, PA, United States
- **Experience:** Expert
- **Salary:** $152,000.0 - $227,900.0
- **Contract:** Permanent contract
- **Skills:** Training Data, Artificial Intelligence, Amazon Web Services, Data Analysis, Application Frameworks, Architectural Patterns, Microsoft Azure, Big Data, Cloud Computing, Continuous Integration, Information Engineering, Data Governance, Data Visualization, R (Programming Language), Python (Programming Language), Machine Learning, Scrum Methodology, Power BI, Tensorflow, Standard Sql, DataOps, Software Deployment, Tableau (Software), Google Cloud, Pytorch, Generative AI, Scikit Learn, Information Technology, Data Pipelines
- **Published:** October 22, 2026
- **Apply:** https://dejobs.org/x/x/3AA5B58C8428450381CAB9F7A3DD8704/job/

## About the Role

* Lead Teams & Strategic Partnerships - Build and develop high-performing Data Engineering and Applied Data Science teams while partnering across business, architecture, platform, security, and technology functions to align priorities, communicate outcomes, and drive execution.REQUIREMENTS:* Advanced Degree plus 6 years of experience, or Bachelor's Degree plus 8 years of experience in data science and analytics leadership roles* Preferred Fields of Study: Data Science, Computer Science, Statistics, Mathematics, Analytics, or related field* Advanced certifications in data science, machine learning, or AI technologies are valued* Strong expertise in machine learning, AI, and statistical modeling techniques* Advanced proficiency in programming languages including Python, R, and SQL* Experience with major AI/ML frameworks (TensorFlow, PyTorch, scikit-learn)* Demonstrated success leading and mentoring data science teams* Expertise in data visualization tools (Tableau, Power BI) and BI
platforms* Proven track record of implementing large-scale data science solutions* Experience with cloud platforms (AWS, Azure, GCP) and big data technologies* Strong knowledge of data governance and regulatory compliance* Excellent project management and cross-functional collaboration skills* Outstanding communication abilities to translate technical concepts for diverse audiences* Experience with Agile/Scrum methodologies* Ability to travel up to 20% as needed* Proven ability to deliver business results through data-driven insights* Experience with automation tools and process optimization* Strong problem-solving and analytical thinking capabilities

## Description

The Senior Manager - Data Engineering & Applied Data Science is responsible for leading the strategy, design, and delivery of enterprise data products, applied data science, and AI solutions that enable business growth and digital transformation. This role leads a multidisciplinary team responsible for Data Engineering and Applied Data Science, transforming business challenges into scalable, production-ready data and AI products.

The successful candidate combines strong technical leadership with business acumen to design data products that address business needs, build AI-ready data foundations, develop advanced analytics and machine learning solutions, and continuously evolve capabilities through the adoption of emerging technologies and engineering best practices. This role is also accountable for ensuring enterprise data is AI-ready-standardized, contextualized, governed, and optimized for analytics, machine learning, and Generative AI use cases.

 * Lead Data & AI Solution Delivery - Translate business priorities into scalable enterprise data products, advanced analytics, machine learning, and AI solutions that deliver measurable business value.
 * Drive Data Engineering Excellence - Lead the design and delivery of reliable, secure, and scalable data pipelines and products while establishing engineering standards across DataOps, CI/CD, automation, observability, and cloud-native development.
 * Advance Applied Data Science & AI - Guide the development and production deployment of machine learning, Generative AI, and advanced analytics solutions, including reusable frameworks and scalable AI capabilities.
 * Build AI-Ready Data Foundations - Ensure enterprise data products are trusted, governed, contextualized, and optimized for analytics and AI through strong data quality, metadata, lineage, semantic models, and accessibility.
 * Shape Technology Strategy & Innovation - Develop technology roadmaps and evaluate emerging technologies and architectural patterns that accelerate AI adoption, improve engineering productivity, and strengthen enterprise data capabilities.