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Remote Data Scientist 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.

LT Apparel Group

Data Analyst

Milford, CT · Remote

$22 - $28/hr

Remote (U.S. Based Preferred) Job Type: Part-Time (Flexible Hours) Pay: $22-$28 per hour (based on experience) Company: LT Apparel Group About the Role LT Apparel Group is hiring a remote Data ...

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All JobsData Scientist JobsRemote Data Scientist JobsRemote Data Scientist Jobs in Hamden, CT

Remote Data Scientist information

See Hamden, CT salary details

$37.3K

$122K

$195.4K

How much do remote data scientist jobs pay per year?

As of Aug 29, 2026, the average yearly pay for remote data scientist in Hamden, CT is $122,025.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,900.00 and $135,200.00 per year, depending on experience, location, and employer.

What is a remote data scientist?

Remote data scientists are professionals who analyze and interpret complex data while working outside of a traditional office environment, typically from home or another remote location. They use statistical methods, machine learning, and programming to extract insights from data, helping organizations make data-driven decisions. Remote data scientists collaborate with teams virtually, often using tools for communication, data analysis, and project management. This flexible work arrangement allows for talent from anywhere to contribute to companies worldwide, provided they have reliable internet and the necessary technical skills.

What does a remote data scientist do?

Remote data scientists collect, confirm, and interpret data to determine useful information for their employer. Unlike in-house data scientists, remote data scientists work outside the office, either from home or another location with Wi-Fi accessibility. Remote data scientists help organizations identify patterns and trends in their data to provide information about lucrative opportunities, necessary improvements, and potential innovations. The information they get from the records they gather helps businesses make decisions in critical areas, such as product development, sales and marketing techniques, and client retention. You find remote data scientists in many different industries, including pharmaceuticals, manufacturing, and banking.

What key skills and qualifications are 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, usually demonstrated through a relevant degree. Familiarity with programming languages like Python or R, experience with machine learning frameworks, and knowledge of data visualization tools are typically required, along with certifications such as Microsoft Certified: Azure Data Scientist Associate or Google Professional Data Engineer. Excellent communication, problem-solving abilities, and self-motivation are critical soft skills for collaborating remotely and delivering insights to stakeholders. These skills are crucial for effectively analyzing data, building predictive models, and driving data-driven decisions in a distributed work environment.

How does a remote data scientist typically collaborate with team members across different time zones?

As a remote data scientist, effective collaboration across time zones often involves leveraging asynchronous communication tools like Slack, project management platforms, and version control systems such as Git. Regular virtual meetings are scheduled to accommodate overlapping hours, and clear documentation becomes crucial for keeping everyone aligned. Proactive communication, sharing progress updates, and setting clear expectations help ensure seamless teamwork despite geographical differences. This structure allows remote data scientists to contribute meaningfully while maintaining flexibility in their work schedules.

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

AspectRemote Data ScientistRemote Data Analyst
Required CredentialsDegree in Data Science, Statistics, or related field; often requires programming skills in Python or RDegree in Analytics, Business, or related field; may require proficiency in Excel, SQL, and visualization tools
Work EnvironmentResearch-focused, developing models, machine learning, and predictive analyticsData interpretation, reporting, and visualization to support business decisions
Employer & Industry UsageTech companies, finance, healthcare, and e-commerceRetail, marketing, finance, and consulting firms

Remote Data Scientists focus on building models and advanced analytics, while Remote Data Analysts interpret data and create reports. Both roles require strong analytical skills but differ in technical depth and project scope.

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Remote Data Scientist jobs near you
Infographic showing various Remote Data Scientist 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, with an average salary of $122,025 per year, or $58.7 per hour.

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 11 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.