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Remote Data Science Jobs in Hamden, CT (NOW HIRING)

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 information

What is remote data science?

Remote data science refers to the practice of performing data analysis, modeling, and interpretation tasks from a location outside of a traditional office, such as from home or a co-working space. Remote data scientists use tools like Python, R, and SQL to analyze data, build predictive models, and communicate insights to stakeholders, all while collaborating virtually with their teams. This setup offers flexibility and can increase access to global job opportunities, but also requires strong self-motivation and communication skills to be effective.

What are the qualifications to get a remote data science job?

The qualifications for a remote data scientist depend in large part on your employer and their industry. Most employers expect remote data science professionals to have at least a bachelor’s degree in statistics, math, computer science, or a related field. Some expect postgraduate degrees in a field like data mining or machine learning or demonstrable skills in these areas. As a remote worker, you need access to relevant programs and an internet connection. You may also want to pursue certification, such as becoming a Certified Analytics Professional (CAP).

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

To thrive as a Remote Data Scientist, you need strong analytical skills, proficiency in statistics, and a solid background in mathematics or computer science, often supported by a relevant degree. Expertise in programming languages such as Python or R, familiarity with machine learning libraries, and experience with cloud-based data platforms are typically required. Excellent communication, self-motivation, and time management skills help you effectively collaborate and deliver results in a remote environment. These skills ensure accurate data analysis, meaningful insights, and successful teamwork despite physical distance.

How do remote data scientists typically collaborate with cross-functional teams to deliver insights?

Remote data scientists often work closely with product managers, engineers, and business analysts using digital collaboration tools such as Slack, Zoom, and project management platforms. Regular virtual meetings, code sharing via Git repositories, and clear documentation are essential to ensure alignment and transparency. While working remotely can present challenges in communication, proactive updates and scheduled syncs help foster strong teamwork and keep projects on track.

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

AspectRemote Data ScienceRemote Data Analyst
Required CredentialsDegree in Data Science, Statistics, or related field; programming skills in Python/R; knowledge of machine learningDegree in Statistics, Mathematics, or related field; proficiency in Excel, SQL, and data visualization tools
Work EnvironmentCollaborative teams, research-focused, often involves building models and algorithmsData reporting, visualization, and interpreting data trends for decision-making
Employer & Industry UsageTech companies, finance, healthcare, e-commerceMarketing agencies, retail, finance, healthcare

Remote Data Science involves developing predictive models and advanced analytics, requiring programming and machine learning skills. Remote Data Analysts focus on interpreting data, creating reports, and visualizations. While both roles analyze data remotely, Data Scientists typically handle more complex modeling tasks, whereas Data Analysts focus on data interpretation and reporting.

Can I work remotely as a data scientist?

Yes, many data scientist roles are available as remote positions, especially in companies that prioritize flexible work arrangements. Remote data scientists typically need strong skills in programming, data analysis, and tools like Python or R, and may require familiarity with cloud platforms and collaboration tools. Availability depends on the employer's policies and the specific job requirements.
More about Remote Data Science jobs

What are the most commonly searched types of Data Science jobs in Hamden, CT?

The most popular types of Data Science jobs in Hamden, CT are:

What are popular job titles related to Remote Data Science jobs in Hamden, CT?

For Remote Data Science jobs in Hamden, CT, the most frequently searched job titles are:

What cities near Hamden, CT are hiring for Remote Data Science jobs?

Cities near Hamden, CT with the most Remote Data Science job openings:

Remote Data Science jobs near you
Infographic showing various Remote Data Science job openings in Hamden, CT as of August 2026, with employment types broken down into 1% As Needed, 81% Full Time, 16% Part Time, and 2% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution.

Data Scientist - Clinical Analytics (Remote)

Fairfield, CT • On-site, Remote

Penfield Search Partners
Recruiting and Staffing Services • 11 - 50 employees
Learn more about Penfield Search Partnersexternal

Full-time

Posted 27 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.