What are the responsibilities and job description for the Staff Machine Learning Engineer position at Thermo Fisher Scientific?
Job Details
Work Schedule
Standard (Mon-Fri)
Environmental Conditions
Office
Job Description
COMPANY: Thermo Fisher Scientific Inc.
LOCATION: 300 Industry Drive, Pittsburgh, PA 15275
TITLE: Staff Machine Learning Engineer
HOURS: Monday to Friday, 8:00 am to 5:00 pm
DUTIES: Build, evaluate, productionize, and optimize advanced data algorithms to enable machine learning solutions for business applications including customer acquisition, upsell and cross-sell, customer retention, recommendation engines, and others.
Architect and code proprietary machine learning models from the ground up when pre-built frameworks and libraries are insufficient for business requirements.
Validate, stress-test, and troubleshoot ML components that are employed across different applications, and write automated tests (unit, integration, functional) to ensure the stability and accuracy of ML solutions.
Develop and implement monitoring protocols to track model performance, ensuring high accuracy, relevance, and adaptability.
Analyze and interpret performance metrics to guide improvements and ensure ML system accuracy and reliability.
Identify and implement code optimizations to reduce model runtime and improve code performance.
Be a technical leader on the ML team, mentoring less experienced data scientists along with defining team best practices and processes.
Collaborate with junior data scientists to deploy ML solutions into production environments, integrating them with existing systems and workflows.
Establish team standards for reusable frameworks to streamline model building, deployment, and monitoring.
Share ideas across the team to come up with the best algorithms and approaches.
REQUIREMENTS: MINIMUM Education Requirement: Master's degree in Statistical Practice, Data Science, or related field of study. MINIMUM Experience Requirement: 3 years of relevant experience in data science, analytics, statistics, applied math, or related experience. Alternative Education and Experience Requirement: Bachelor's degree in Statistical Practice, Data Science, or related field of study plus 5 years of relevant experience in data science, analytics, statistics, applied math, or related experience. Required knowledge or experience with: Experience with sales operations tools (e.g., Salesforce); Integrating machine learning models into CRM systems; Mathematical concepts and numerical estimation; Quantitative analysis; Python (or PySpark) and SQL; Experience building and delivering models using Databricks; Experience developing production-quality code in PySpark; Experience working in Agile/Sprint environments; Experience with generative AI techniques and tools, such as transformer models (e.g., GPT, BERT), to enhance model insights, personalization, or content generation within sales applications; Experience with version control / source code management systems (Git, GitHub). Employer will accept any suitable combination of education, experience or training. Experience may be gained concurrently through graduate level course work and/or internships.
Standard (Mon-Fri)
Environmental Conditions
Office
Job Description
COMPANY: Thermo Fisher Scientific Inc.
LOCATION: 300 Industry Drive, Pittsburgh, PA 15275
TITLE: Staff Machine Learning Engineer
HOURS: Monday to Friday, 8:00 am to 5:00 pm
DUTIES: Build, evaluate, productionize, and optimize advanced data algorithms to enable machine learning solutions for business applications including customer acquisition, upsell and cross-sell, customer retention, recommendation engines, and others.
Architect and code proprietary machine learning models from the ground up when pre-built frameworks and libraries are insufficient for business requirements.
Validate, stress-test, and troubleshoot ML components that are employed across different applications, and write automated tests (unit, integration, functional) to ensure the stability and accuracy of ML solutions.
Develop and implement monitoring protocols to track model performance, ensuring high accuracy, relevance, and adaptability.
Analyze and interpret performance metrics to guide improvements and ensure ML system accuracy and reliability.
Identify and implement code optimizations to reduce model runtime and improve code performance.
Be a technical leader on the ML team, mentoring less experienced data scientists along with defining team best practices and processes.
Collaborate with junior data scientists to deploy ML solutions into production environments, integrating them with existing systems and workflows.
Establish team standards for reusable frameworks to streamline model building, deployment, and monitoring.
Share ideas across the team to come up with the best algorithms and approaches.
REQUIREMENTS: MINIMUM Education Requirement: Master's degree in Statistical Practice, Data Science, or related field of study. MINIMUM Experience Requirement: 3 years of relevant experience in data science, analytics, statistics, applied math, or related experience. Alternative Education and Experience Requirement: Bachelor's degree in Statistical Practice, Data Science, or related field of study plus 5 years of relevant experience in data science, analytics, statistics, applied math, or related experience. Required knowledge or experience with: Experience with sales operations tools (e.g., Salesforce); Integrating machine learning models into CRM systems; Mathematical concepts and numerical estimation; Quantitative analysis; Python (or PySpark) and SQL; Experience building and delivering models using Databricks; Experience developing production-quality code in PySpark; Experience working in Agile/Sprint environments; Experience with generative AI techniques and tools, such as transformer models (e.g., GPT, BERT), to enhance model insights, personalization, or content generation within sales applications; Experience with version control / source code management systems (Git, GitHub). Employer will accept any suitable combination of education, experience or training. Experience may be gained concurrently through graduate level course work and/or internships.
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