Data Scientist III,

Job description

Work Schedule Standard (Mon-Fri) Environmental Conditions Office Job Description As part of the Thermo Fisher Scientific team, you'll discover meaningful work that makes a positive impact on a global scale. Join our colleagues in bringing our Mission to life every single day to enable our customers to make the world healthier, cleaner and safer. We provide our global teams with the resources needed to achieve individual career goals while helping to take science a step beyond by developing solutions for some of the world's toughest challenges, like protecting the environment, making sure our food is safe or helping find cures for cancer. DESCRIPTION Join our team at Thermo Fisher Scientific as a Data Scientist III within the Data Science Center of Excellence. In this role, you will help design, build, and operate the enterprise platform that enables scalable data science and AI solutions across the organization. Working at the intersection of analytics, software engineering, and cloud infrastructure, you will partner closely with Data Engineers, Analytics Engineers, Product Managers, and business stakeholders to deliver reliable, production-ready capabilities that generate measurable business value. This role combines advanced analytics with platform engineering and operational excellence. In addition to developing machine learning and AI solutions, you will contribute to the automation, orchestration, monitoring, and support of the data science platform. You will help establish engineering best practices that enable reliable, scalable, and maintainable production workloads. As part of a multidisciplinary technical team, you will contribute to the operational health, reliability, and continuous improvement of the enterprise data science platform. Working closely with Data Engineers, Analytics Engineers, Product Managers, and technical leadership, you will help ensure production solutions are scalable, resilient, and well-supported throughout their lifecycle. KEY RESPONSIBILITIES Solution Design & Delivery Design, develop, and deploy machine learning, AI, and advanced analytics solutions that address commercial and operational business needs. Translate business requirements into scalable analytical solutions and actionable recommendations. Partner with Data Engineers, Analytics Engineers, Product Managers, and business stakeholders to design technical solutions that balance business value, scalability, and maintainability. Apply statistical analysis, machine learning, and AI techniques to solve complex business problems and deliver measurable outcomes. Platform Engineering & Automation Build and maintain reusable data science components, automated workflows, and production pipelines using Databricks, Spark, and AWS. Develop workflow orchestration, deployment automation, and operational tooling that improve platform efficiency, reliability, and reduce manual effort. Contribute to platform architecture, engineering standards, and technical best practices supporting enterprise AI solutions. Collaborate with Data Engineers to integrate analytical solutions into production systems and enterprise data platforms. Platform Operations & Engineering Contribute to the operation, monitoring, and continuous improvement of the enterprise data science platform. Collaborate with the broader technical team to maintain reliable production pipelines, scheduled workflows, and deployed machine learning solutions. Investigate production issues, perform root cause analysis, and implement improvements that increase platform reliability, observability, and operational efficiency. Participate in release activities, disaster recovery planning, and operational readiness to support resilient production systems. Collaboration & Technical Leadership Partner closely with Data Engineers, Analytics Engineers, Product Managers, and technical leadership to design scalable, maintainable solutions. Communicate technical concepts, solution designs, and analytical findings to both technical and business audiences. Contribute to documentation, code quality, knowledge sharing, and engineering best practices across the Data Science Center of Excellence. Stay current with emerging technologies and recommend improvements that strengthen the team's capabilities and platform. REQUIREMENTS Minimum Qualifications Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field. 3+ years of professional experience in data science, machine learning engineering, analytics engineering, data engineering, or a related technical discipline. Strong programming skills in Python and SQL. Experience developing and deploying production data science or analytics solutions. Hands-on experience with Databricks, Apache Spark, and AWS. Experience building or supporting data pipelines, automated workflows, or production data platforms. Experience with workflow orchestration, automation, or job scheduling. Experience monitoring, troubleshooting, and supporting production systems. Experience with Git/GitHub and modern software development practices. Strong communication and collaboration skills with the ability to work effectively across technical and business teams. Preferred Qualifications Master's or PhD in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field and 2+ years of relevant professional experience . Experience building recommendation systems or AI-enabled commercial solutions. Experience with MLflow and machine learning lifecycle management. Familiarity with Salesforce data models and CRM workflows. Experience implementing CI/CD, infrastructure automation, or MLOps practices. Experience supporting cloud-based analytics platforms in a production environment. Experience working in life sciences, healthcare, or other regulated industries.