Scientist Data - Hybrid - Eagan, MN

SOLVENTUM•Eagan, MN

1d•$106,000 - $145,750•Hybrid

About The Position

At Solventum, we enable better, smarter, safer healthcare to improve lives. As a new company with a long legacy of creating breakthrough solutions for our customers’ toughest challenges, we pioneer game-changing innovations at the intersection of health, material and data science that change patients' lives for the better while enabling healthcare professionals to perform at their best. Because people, and their wellbeing, are at the heart of every scientific advancement we pursue. We partner closely with the brightest minds in healthcare to ensure that every solution we create melds the latest technology with compassion and empathy. Because at Solventum, we never stop solving for you. The Impact You’ll Make in this Role As a Data Scientist within the Product Informatics & Modeling group you will have the opportunity to tap into your curiosity and collaborate with some of the most innovative and diverse people around the world. Here, you will make an impact by: Developing machine learning and statistical models that extract insights from chemical, materials, and experimental datasets supporting accelerated formulation design, process optimization, and product performance prediction Integrating experimental and simulation-generated data to improve scientific understanding and accelerate decision making Designing and maintaining data pipelines, workflows, and analytical tools that enable reproducible scientific analyses Collaborating with R&D and business teams to frame scientific questions, develop modeling strategies, and translate research challenges into data-driven solutions. Communicating results and recommendations to both technical and non-technical stakeholders

Requirements

Ph.D. in Chemical Engineering, Chemistry, Materials Science, Computational Chemistry, Data Science, or STEM discipline with an emphasis on quantitative modeling, machine learning, or computational science confirmed before negotiated start date
M.S. in Data Science with a Bachelor’s Degree in Chemical Engineering, Chemistry, Materials Science, or STEM field confirmed before negotiated start date AND 1 years of experience applying data science, modeling or informatics techniques in a private, public, government or military environment
Bachelor’s Degree or higher in Chemical Engineering, Chemistry, Materials Science, or STEM field confirmed before negotiated start date AND 5 years of experience applying data science, modeling or informatics techniques in a private, public, government or military environment
High School Diploma/GED AND 10 years of experience applying data science, modeling or informatics techniques in a private, public, government or military environment
Must be legally authorized to work in country of employment without sponsorship for employment visa status (e.g., H1B status).

Nice To Haves

Strong foundation in organic chemistry, synthetic chemistry, polymer chemistry, materials science, or related chemical disciplines
Experience developing statistical, machine learning, or predictive modeling solutions using Python and modern scientific computing libraries.
Demonstrated curiosity, scientific rigor, and ability to solve ambiguous research problems
Experience working and deploying machine learning solutions within a healthcare R&D environment
Ability to work and communicate effectively with multidisciplinary teams spanning R&D, Manufacturing, Quality, and Regulatory

Responsibilities

Developing machine learning and statistical models that extract insights from chemical, materials, and experimental datasets supporting accelerated formulation design, process optimization, and product performance prediction
Integrating experimental and simulation-generated data to improve scientific understanding and accelerate decision making
Designing and maintaining data pipelines, workflows, and analytical tools that enable reproducible scientific analyses
Collaborating with R&D and business teams to frame scientific questions, develop modeling strategies, and translate research challenges into data-driven solutions.
Communicating results and recommendations to both technical and non-technical stakeholders