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Internship Biotech Data Analyst Jobs in Ridgefield, CT

Synchrony

BLP Intern - Data Analytics

Stamford, CT

Data Analytics BLP Internship Experience: What You'll Accomplish This internship is for students who are looking to get a critical jump start on a career in Data Analytics, working side by side with ...

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Internship Biotech Data Analyst information

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$11

$22

$41

How much do internship biotech data analyst jobs pay per hour?

As of Aug 27, 2026, the average hourly pay for internship biotech data analyst in Ridgefield, CT is $22.41, according to ZipRecruiter salary data. Most workers in this role earn between $17.26 and $24.42 per hour, depending on experience, location, and employer.

What does an internship biotech data analyst do?

An Internship Biotech Data Analyst supports research and development teams in biotechnology companies by collecting, processing, and analyzing complex biological data. They use statistical tools and software to interpret experimental results, identify trends, and help drive decision-making in projects such as drug discovery, genomics, or clinical trials. Interns often work alongside experienced data analysts and scientists, gaining hands-on experience with real-world datasets and contributing to ongoing research efforts.

What types of projects or data sets might I work with as an internship biotech data analyst?

As an Internship Biotech Data Analyst, you can expect to work with real-world biological or clinical data, such as genomic sequences, protein structures, or patient trial results. Projects often involve cleaning, analyzing, and visualizing these datasets to support research or product development. You'll likely collaborate closely with scientists, senior analysts, and sometimes software engineers to interpret findings and present actionable insights. This hands-on experience not only strengthens your technical skills but also helps you understand how data analysis drives innovation in the biotech industry.

What are the key skills and qualifications needed to thrive as an internship biotech data analyst, and why are they important?

To thrive as an Internship Biotech Data Analyst, you need a foundation in biology or biotechnology, statistical analysis, and data interpretation, typically gained through coursework or relevant internships. Familiarity with data analysis tools such as Python, R, Excel, and experience with bioinformatics databases or platforms like BLAST is highly beneficial. Strong attention to detail, problem-solving abilities, and effective communication skills make candidates stand out in this role. These competencies are crucial for accurately analyzing complex biological data, supporting research projects, and conveying findings to multidisciplinary teams.

What is the difference between Internship Biotech Data Analyst vs Biotech Data Scientist?

AspectInternship Biotech Data AnalystBiotech Data Scientist
Required CredentialsEnrolled in or recent graduate of related degree (e.g., biology, bioinformatics, data analysis)Advanced degree (Master's or PhD) in data science, bioinformatics, or related fields
Work EnvironmentInternship setting in biotech or pharmaceutical companies, labs, or research institutionsFull-time role in biotech firms, research centers, or biotech startups
Employer & Industry UsageUsed for entry-level training, skill development, and gaining industry experienceUsed for advanced data analysis, modeling, and research projects

The Internship Biotech Data Analyst is an entry-level position focused on gaining practical experience in biotech data analysis, often requiring relevant coursework or a recent degree. In contrast, a Biotech Data Scientist typically holds advanced degrees and performs complex data modeling and research tasks. The internship provides foundational skills, while the data scientist role involves more specialized expertise and responsibilities.

Can a biotechnology student become a data analyst?

Yes, a biotechnology student can become a data analyst by developing skills in data management, statistical analysis, and tools like Excel, SQL, or Python. Gaining knowledge of data visualization and relevant industry applications can also improve job prospects in biotech data analysis roles.

How do I get an internship at a biotech data analyst?

To secure an internship as a biotech data analyst, candidates should have a strong background in biology, data analysis, and programming languages like Python or R. Gaining experience through relevant coursework, certifications, or projects, and applying to internships via company websites or job portals, can improve chances. Strong analytical skills and familiarity with biotech databases and tools are also beneficial.

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Internship Biotech Data Analyst jobs near you

Technical Clinical Data Manager /Data Analyst (Remote)

Penfield Search Partners

Fairfield, CT • Remote

Full-time

Posted 27 days ago

Job description

Contact: Neisha Camacho/Terra Parsons - 
No 3rd party candidates

We are partnering with a growing biotech organization seeking a Clinical Data Manager/Data Analyst to support ongoing and upcoming clinical programs across multiple therapeutic areas.

This role will sit within a highly collaborative clinical development team and is ideal for a hands-on, analytical Clinical Data Manager who enjoys digging into the data, identifying trends or issues proactively, and developing creative solutions to improve data quality and study execution. The client is seeking someone resourceful and technically curious — not just process-oriented — with the ability to independently explore datasets, generate meaningful reports, and surface potential issues early.

The ideal candidate will bring a blend of traditional clinical data management expertise along with strong data review, querying, and analytical skills. Experience with R, SQL, SAS, or other data interrogation and reporting tools is highly desirable.

Key Responsibilities

  • Support clinical data management activities across studies from start-up through database lock
  • Perform hands-on data review and exploratory analysis to identify data trends, inconsistencies, missing data patterns, and potential study risks
  • Develop custom reports, listings, and data visualizations to support proactive data cleaning and study oversight
  • Utilize R, SQL, SAS, or similar tools to query, analyze, and troubleshoot clinical datasets
  • Partner with CROs and vendors to ensure high-quality, timely data delivery
  • Collaborate cross-functionally with Clinical Operations, Biostatistics, Statistical Programming, Medical, and Safety teams on data review strategies and issue resolution
  • Contribute to development and review of key deliverables including:
    • Data Management Plans (DMPs)
    • eCRF design and completion guidelines
    • Edit checks and query logic
    • User Acceptance Testing (UAT)
    • Data review plans and cleaning processes
  • Support ongoing query management and ensure data integrity, consistency, and inspection readiness
  • Assist with implementation of data standards, reporting enhancements, and process improvements
  • Work independently to investigate data anomalies and recommend practical solutions in a fast-paced biotech environment

Qualifications

  • BS in a scientific, technical, or clinical discipline
  • Approximately 5–8 years of clinical data management experience within biotech, pharma, or CRO environments
  • Strong hands-on experience reviewing and interrogating clinical trial data
  • Experience using R, SQL, SAS, or other querying/reporting tools to analyze clinical data and generate custom reports
  • Experience with EDC systems such as Medidata Rave, Oracle, or Veeva
  • Working knowledge of CDISC standards (SDTM/ADaM) and downstream data usage
  • Experience supporting Phase II and/or III clinical trials; CNS experience is a plus
  • Exposure to CRO/vendor oversight
  • Understanding of clinical data structures and dictionaries including MedDRA and WHODrug
  • Strong critical thinking and problem-solving skills with the ability to work independently
  • Resourceful, proactive, detail-oriented, and comfortable operating in a dynamic biotech environment
  • Strong communication and cross-functional collaboration skills