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Data Scientist Biochemistry Jobs in Springfield, MO

Danaher

R&D Internships

Springfield, MO · On-site

Supporting immunoassay development teams with experimental setup, data collection, and ... Students currently pursuing a bachelor's degree in Chemistry, Biochemistry, Biological Sciences, or ...

All JobsData Scientist Biochemistry JobsData Scientist Biochemistry Jobs in Springfield, MO

Data Scientist Biochemistry information

See Springfield, MO salary details

$34.1K

$111.6K

$178.7K

How much do data scientist biochemistry jobs pay per year?

As of Aug 21, 2026, the average yearly pay for data scientist biochemistry in Springfield, MO is $111,646.00, according to ZipRecruiter salary data. Most workers in this role earn between $89,600.00 and $123,700.00 per year, depending on experience, location, and employer.

What does a data scientist in biochemistry do?

A Data Scientist in Biochemistry applies data analysis, machine learning, and statistical techniques to large sets of biochemical data. They work with experimental results, genomic sequences, protein structures, and other biological datasets to uncover patterns, make predictions, and support scientific discoveries. Their work often involves collaboration with biochemists to design experiments, interpret results, and contribute to advancements in areas like drug discovery and understanding disease mechanisms.

How does a data scientist in biochemistry typically collaborate with laboratory researchers and other scientific teams?

As a Data Scientist in Biochemistry, you'll frequently work alongside laboratory researchers, chemists, and biologists to interpret experimental results and develop data-driven insights. Collaboration often involves translating complex biological questions into computational problems, analyzing large datasets from experiments, and communicating findings in a way that supports ongoing research. Effective teamwork and clear communication are crucial, as you'll help bridge the gap between experimental science and analytical modeling, ensuring that data analyses are aligned with scientific objectives.

What are the key skills and qualifications needed to thrive as a data scientist in biochemistry, and why are they important?

To thrive as a Data Scientist in Biochemistry, you need a strong background in biochemistry or molecular biology, advanced statistical analysis, and programming skills (often with a master's or Ph.D. in a related field). Familiarity with tools such as Python, R, machine learning frameworks, and bioinformatics databases is typically required, along with experience using laboratory data management systems. Exceptional problem-solving, critical thinking, and communication skills set candidates apart, enabling them to interpret complex biological data and collaborate across scientific teams. These skills are crucial for transforming raw biochemical data into actionable insights that drive research and innovation in life sciences.

What is the difference between Data Scientist Biochemistry vs Data Analyst Biochemistry?

AspectData Scientist BiochemistryData Analyst Biochemistry
Required CredentialsDegree in Biochemistry, Data Science, or related fields; proficiency in programming and statistical toolsDegree in Biochemistry, Life Sciences, or related fields; basic data analysis skills
Work EnvironmentResearch labs, biotech companies, pharmaceutical firms, often involving complex data modelingLaboratories, research institutions, healthcare settings, focusing on data reporting and visualization
Employer & Industry UsageUsed in biotech, pharma, research institutions for advanced data modeling and predictive analyticsCommon in healthcare, research labs for data reporting, trend analysis, and basic statistical tasks

While both roles require a background in biochemistry, Data Scientist Biochemistry focuses on advanced data modeling, machine learning, and predictive analytics, often requiring programming skills. Data Analysts Biochemistry primarily handle data reporting, visualization, and basic statistical analysis. The roles differ mainly in complexity and technical expertise, but both are vital in biotech and healthcare industries.

Can I become a data scientist with a biotech degree?

A biotech degree can provide a strong foundation for a data scientist role in biochemistry, especially if complemented with skills in programming, statistics, and data analysis tools like Python or R. Many data scientists in biotech have backgrounds in life sciences and acquire additional training or certifications in data science to meet job requirements.

What are popular job titles related to Data Scientist Biochemistry jobs in Springfield, MO?

For Data Scientist Biochemistry jobs in Springfield, MO, the most frequently searched job titles are:

Data Scientist Biochemistry jobs near you
Infographic showing various Data Scientist Biochemistry job openings in Springfield, MO as of August 2026, with employment types broken down into 100% Part Time. Highlights an 100% Remote job distribution, with an average salary of $111,646 per year, or $53.7 per hour.

Bioinformatics Research Scientist - AI Reviewer

micro1 AI

Springfield, MO • Remote

$80 - $110/hr

Part-time

Posted 18 days ago

Job description

Role Title: Computational Biology & Cheminformatics Expert


Role Type: Contractor


Location: Remote


micro1 is engaging Computational Biology & Cheminformatics Experts to contribute their expertise to a customer’s computational drug discovery project. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.


Scope of Work

  1. Analyze and interpret small-molecule and drug discovery datasets using advanced computational biology, bioinformatics, and cheminformatics methods.
  2. Curate, annotate, and validate chemical and biological datasets (e.g., ChEMBL, PubChem, DrugBank) to support AI-driven discovery platforms.
  3. Evaluate compound-target interactions, ADMET properties, and lead optimization strategies by integrating chemical, biological, and clinical data sources.
  4. Provide expert insights on structure-activity and structure-property relationships (SAR/SPR), medicinal chemistry approaches, and experimental design considerations.
  5. Build and implement code-based benchmark tasks (e.g., terminal/CLI-based environments) that reflect realistic computational drug discovery scenarios.
  6. Develop reproducible environments (e.g., using Docker) and automated testing pipelines to ensure task correctness and solvability.
  7. Assess and review AI-generated outputs for scientific rigor, accuracy, and practical relevance, delivering detailed written feedback and recommendations.


Preferred Qualifications

  1. Advanced expertise in Computational Biology, Cheminformatics, Medicinal Chemistry, Biochemistry, or related fields; advanced degree (PhD, MSc, PharmD) highly valued but not strictly required.
  2. Strong coding proficiency in Python (beyond analysis scripts), with hands-on experience building tools, pipelines, or testable code; familiarity with Git, GitHub, and Docker.
  3. Extensive experience with cheminformatics toolkits and platforms such as RDKit, KNIME, Schrödinger, OpenEye, or MOE.
  4. Proven track record in small-molecule drug discovery, SAR/QSAR evaluation, ADMET prediction, or virtual screening workflows.
  5. Comfort working with public chemical and bioactivity databases and integrating diverse datasets for scientific analysis.
  6. Demonstrated ability to clearly communicate complex chemical and biological concepts in written feedback and reports.
  7. Experience participating in multidisciplinary and/or remote projects; familiarity with AI-assisted coding tools is a plus.

Apply