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Biotech Data Science Jobs (NOW HIRING)

Stellar IT Group

BioTech Data Engineer

Saint Louis, MO · On-site

$111K - $133K/yr

Biotech Data Engineer Job Type: FTE Work Mode: Remote The Biotech Data Engineer focuses on ... science, and operational use cases * Lead data engineering initiatives that enable AI/ML model ...

Formation Bio

Director, Data Science

Boston, NY · On-site

$235K - $307K/yr

About the Position As the Director of Data Science at Formation Bio, you will be at the forefront ... sciences (biotech, pharma, consulting) * Strong programming skills, particularly in Python

Biogen

Associate Director, AI/Data Science

Cambridge, MA

$176K - $242K/yr

This role will shape data science strategy, develop and evaluate AI/ML solutions, and partner ... As a mid-sized biotechnology company, we provide the stability and resources of a well-established ...

Biogen

Associate Director, AI/Data Science

Cambridge, MA · On-site

$176K - $242K/yr

This role will shape data science strategy, develop and evaluate AI/ML solutions, and partner ... As a mid-sized biotechnology company, we provide the stability and resources of a well-established ...

Formation Bio

Director, Data Science

Boston, MA · On-site

$235K - $307K/yr

About the Position As the Director of Data Science at Formation Bio, you will be at the forefront ... sciences (biotech, pharma, consulting) * Strong programming skills, particularly in Python

Trinity Life Sciences

... Sciences, or related field * CDMP (Certified Data Management Professional) or DCAM (Data Capability Assessment Model) certification preferred * Minimum 5+ years in pharmaceutical or biotech data ...

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Biotech Data Science information

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$37.5K

$122.7K

$196.5K

How much do biotech data science jobs pay per year?

As of Jul 13, 2026, the average yearly pay for biotech data science in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

Can data scientists make $300k?

Biotech data scientists can potentially earn $300,000 or more annually, especially with extensive experience, advanced skills in machine learning and bioinformatics, and working in senior or specialized roles. Compensation varies based on location, company size, and individual expertise, with some senior-level positions reaching or exceeding this salary level.

What are the most common challenges faced by professionals in Biotech Data Science roles?

One of the primary challenges in Biotech Data Science is working with large, complex, and sometimes incomplete biological datasets, which require advanced analytical approaches and careful data curation. Professionals often need to stay current with rapidly evolving technologies and methods, which can be demanding but also rewarding for those who enjoy continuous learning. Collaboration with scientists, engineers, and regulatory teams is common, so adapting communication styles and translating technical findings to diverse audiences is key. Overcoming these challenges leads to meaningful scientific discoveries and significant career growth opportunities.

What are the key skills and qualifications needed to thrive in the Biotech Data Science position, and why are they important?

To thrive in Biotech Data Science, you need a solid background in biology or biotechnology, strong statistical and analytical skills, and experience with data analysis languages like Python or R. Familiarity with bioinformatics tools, sequencing platforms, and data visualization software is often expected, with certifications in data science or related fields considered a plus. Excellent problem-solving, communication, and collaboration skills are essential when working across multidisciplinary teams. These competencies enable effective interpretation of complex biological data, driving innovation and insights in the biotech industry.

What is a biotech data scientist?

A biotech data scientist analyzes biological and medical data to support research and development in the biotechnology industry. They use skills in statistics, programming, and machine learning, often working with tools like Python, R, and SQL to interpret complex datasets and inform decision-making.

What is a Biotech Data Science job?

A Biotech Data Science job involves analyzing complex biological and pharmaceutical data to drive research, innovation, and decision-making. Professionals in this field use machine learning, statistical modeling, and bioinformatics tools to extract insights from genomics, clinical trials, and drug discovery datasets. They collaborate with scientists, engineers, and healthcare professionals to improve treatments, develop new therapies, and optimize bioprocesses. Strong programming skills, domain knowledge in biology or biotechnology, and expertise in data analysis are essential for success in this role.

How can data science be used in biotechnology?

Biotech data scientists analyze large biological datasets to identify patterns, develop predictive models, and optimize processes such as drug discovery, genetic research, and personalized medicine. They use tools like machine learning, statistical analysis, and bioinformatics software to support research and development efforts in biotechnology companies and labs.

Can a biotechnologist become a data scientist?

A biotechnologist can become a data scientist by acquiring skills in programming, statistics, and machine learning, often through additional training or education such as online courses or advanced degrees. Their background in biology and laboratory data can provide a strong foundation for analyzing complex datasets in data science roles within biotech and healthcare industries.
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BioTech Data Engineer

Stellar IT Group

Saint Louis, MO • On-site

$111K - $133K/yr

Full-time

Re-posted 11 days ago

Job description

Overview:
Job Title: Biotech Data Engineer
Job Type: FTE
Work Mode: Remote
The Biotech Data Engineer focuses on designing, building, and maintaining scalable Azure Databricks-based data pipelines and architectures that enable analytics, AI/ML, and reporting across commercial functions. It involves leading data engineering initiatives, ensuring governance and compliance, optimizing performance and costs, and collaborating across teams to advance the company's data and AI strategy. The role reports to the Director of Business Intelligence.
Roles & Responsibilities
  • Design, build, and maintain scalable, reliable, and cost-efficient data pipelines using Azure Databricks in support of analytics, machine learning, data science, and operational use cases
  • Lead data engineering initiatives that enable AI/ML model development, LLM integrations, and AI-driven applications while ensuring scalability and alignment with enterprise and business priorities
  • Architect and implement data integration frameworks across diverse Adtech and Martech ecosystems, incorporating Google Analytics, media campaign data, and third-party marketing APIs like Salesforce
  • Manage and optimize data ingestion, transformation, and storage processes using SQL, Python, and PySpark to integrate structured and unstructured data sources
  • Design and maintain API integrations with internal and external systems, including Python- and PySpark-based services and AI/LLM-powered APIs for advanced analytics and automation
  • Administer and maintain Azure-based data tools and platforms (e.g., Databricks, ADF) to ensure operational excellence, reliability, and security
  • Collaborate with internal stakeholders and external partners to evolve data platform design and architecture that supports advanced analytics, personalization, marketing intelligence, and marketing automation
  • Execute against the company's data and AI strategy by translating strategic goals into technical architecture, design and requirement documents, and implementation roadmaps
  • Ensure data quality, integrity, and consistency through robust validation, monitoring, and alerting mechanisms within Azure Databricks
  • Implement and enforce data governance, security, and compliance standards in collaboration with IT, InfoSec, and data governance teams
  • Partner with analytics, marketing, commercial operations, and core technology/cybersecurity teams to deliver fit-for-purpose commercial data products
  • Monitor and optimize data infrastructure costs, performance, and scalability across Azure cloud environments
  • Develop and maintain architecture documentation, pipeline specifications, and design diagrams for transparency and knowledge sharing with technical and business stakeholders
  • Participate in architecture discussions, design reviews, and CI/CD workflows to ensure high-quality engineering and deployment practices as part of an Agile engineering team
  • Continuously evaluate and recommend new Azure services, designs, improvements, frameworks, and AI integration tools to enhance our data platform
  • Drive automation, observability, and standardization across data workflows to improve efficiency and reduce manual intervention
  • Complete all job duties in compliance with company policy, SOPs, safety rules, and applicable federal, state, and local regulations

Education & Licenses And Experience
Bachelor of science degree required. A minimum of 5 years transferable working experience in the area of operational support of a Data Engineering, Data Architecture, or Cloud Platforms function preferred. The ideal candidate will have recent and relevant experience in the pharma or biotech industry.
Required Competencies & Skills
  • Experience with Azure Cloud (Databricks, DevOps, DataFactory)
  • Pharma / biotech domain experience, specifically within the commercial data space (sales, market access / payer, marketing)
  • Strong hands-on python, pyspark, and SQL skills
  • Direct experience with building and leveraging API integrations in ETL pipeline development
  • Experience integrating with current best-in-class AI models and APIs like OpenAI API, Databricks AI models, etc.
  • Self-driven with ability to independently design end-to-end data pipelines while ensuring architectural best practices
  • Ability to collaborate with a broad set of stakeholders to evaluate the business need and construct a technical design that can enable stakeholder priorities
  • Travel up to 20%

Preferred Competencies & Skills
  • Strong knowledge of and experience with maximizing business value using Databricks Unity Catalog or Databricks One capabilities
  • Familiarity with tools and practices in the Martech and Adtech landscape
  • Knowledge of Consumer, Patient, or HCP data ecosystems
  • Experience with identity providers like LiveRamp, Acxiom, Experian, etc.
  • Experience with custom-built or third-party Customer Data Platforms (CDP)
  • Experience with marketing automation tools

Skills:
Data Engineering

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