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Remote Data Science Internships Jobs in Fairfield, CT

System One

Data Scientist III

Ridgefield, CT · Remote

$45 - $50/hr

Remote-offsite Hours : 40.0 Overview Leading pharmaceutical company looking for an experienced Data Scientist III. Ideal candidates should have a PhD degree and experience programming experience with ...

System One

Data Scientist III

Ridgefield, CT · Remote

$45 - $50/hr

Remote-offsite Hours : 40.0 Overview Leading pharmaceutical company looking for an experienced Data Scientist III. Ideal candidates should have a PhD degree and experience programming experience with ...

System One

Data Scientist III

Ridgefield, CT · Remote

$45 - $50/hr

Remote-offsite Hours : 40.0 Overview Leading pharmaceutical company looking for an experienced Data Scientist III. Ideal candidates should have a PhD degree and experience programming experience with ...

Gartner

Associate Data Scientist

Stamford, CT · On-site +1

$62K - $63K/yr

... data science and machine learning techniques through professional work, internships, or research. * Experience with Python, SQL, statistical analysis, and databases. * Curiosity and a growth mindset ...

Linde Group

Data Scientist

Danbury, CT · Remote

Write SQL queries to analyze data and support systemoperations, utilizing technologies, including ... Remote work permitted from any location in theU.S.

Linde

Data Scientist

Danbury, CT · Remote

Write SQL queries to analyze data and support systemoperations, utilizing technologies, including ... Remote work permitted from any location in theU.S.

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Remote Data Science Internships information

What is the difference between Remote Data Science Internships vs Remote Data Analyst Internships?

AspectRemote Data Science InternshipsRemote Data Analyst Internships
Required SkillsProgramming (Python, R), statistics, machine learningData visualization, SQL, basic statistics
Work EnvironmentCollaborative teams, research-focusedBusiness analysis, reporting
Industry UsageTech, finance, healthcareRetail, marketing, finance

Remote Data Science Internships focus on developing predictive models and advanced analytics, requiring programming and machine learning skills. Remote Data Analyst Internships emphasize data interpretation, visualization, and reporting. Both roles are remote, but they serve different functions within organizations, with data science being more technical and analytical, while data analysis centers on business insights.

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Remote Data Science Internships jobs near you

Data Scientist - Clinical Analytics (Remote)

Penfield Search Partners

Fairfield, CT • On-site, Remote

Full-time

Posted 16 days ago

Job description

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

This hands-on role sits at the intersection of Data Science, Biostatistics, Statistical Programming, and Clinical Data Management. The ideal candidate combines strong programming skills with clinical study experience and can build practical solutions that improve how teams access, analyze, visualize, and work with clinical data.

This individual will support ongoing studies while building technical infrastructure, automation, and reusable tools for a growing Biometrics organization. The successful candidate will be forward-thinking, collaborative, and comfortable introducing modern approaches in a cross-functional environment.

Primary Responsibilities

  • Develop data science solutions, analytical tools, dashboards, and visualizations to support clinical studies and data review.
  • Build reusable tools, workflows, and infrastructure for Statistical Programming, Biostatistics, Data Management, and other teams.
  • Build automated workflows using GitHub/GitHub Actions for quality checks, validation, code review, testing, and deployment.
  • Work with databases and data sources to support integration, analysis, and visualization.
  • Apply R and SAS to clinical data and analytical challenges and use Python when appropriate.
  • Develop and maintain interactive applications using R Shiny.
  • Partner across Biometrics and other clinical functions to understand study needs and develop effective technical solutions.
  • Identify opportunities to automate manual processes and improve efficiency.
  • Establish effective Git/GitHub, version control, and collaborative development practices.
  • Help team members adopt modern programming, automation, and application development practices.
  • Contribute technical expertise as Data Science and clinical analytics capabilities grow.

Qualifications

  • Bachelor's or Master's degree in Data Science, Statistics, Biostatistics, Computer Science, or a related quantitative field.
  • Significant Data Science, Statistical Programming, Clinical Analytics, or related experience within pharma, biotech, or clinical research.
  • Advanced programming experience in R and SAS.
  • Experience building and maintaining R packages.
  • Strong hands-on experience with Git/GitHub and GitHub Actions.
  • Experience developing analytical applications, dashboards, and visualizations, including R Shiny.
  • Experience with databases and integrating data into analytical workflows.
  • Strong understanding of version control, code review, testing, and automation.
  • Experience building reusable technical solutions and infrastructure.
  • Experience working with clinical study data and supporting study teams.
  • Strong cross-functional communication skills.
  • Ability to work independently in a small, growing organization while remaining highly collaborative.

Preferred Experience

  • Working knowledge of Python.
  • Experience building infrastructure, frameworks, or reusable tools for programmers, statisticians, or data scientists.
  • Experience automating development and quality-control processes.
  • Experience helping teams adopt Git/GitHub, R Shiny, automation, or other modern development practices.

Key Success Factors

  • Hands-on: Personally builds solutions rather than only directing others.
  • Forward-thinking: Brings ideas and seeks more efficient ways to solve problems.
  • Practical: Selects technology based on the problem.
  • Collaborative: Works effectively across clinical and technical functions.
  • Builder: Comfortable establishing tools, infrastructure, and new ways of working.
  • Self-directed: Identifies needs, proposes solutions, and drives work forward.