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Data Scientist Research Analyst Jobs (NOW HIRING)

HOF Capital

Data Scientist / Researcher

Manhattan, NY

$160K - $470K/yr

... research & analyses on future market opportunities, in order to identify the most promising ones. * Assist with investment due diligence by: (1) collecting the data needed for accurate market ...

HOF Capital

Data Scientist / Researcher

Manhattan, NY

$160K - $470K/yr

... research & analyses on future market opportunities, in order to identify the most promising ones. * Assist with investment due diligence by: (1) collecting the data needed for accurate market ...

The Pennsylvania State University

Collaborate with geospatial data analysts to understand and automate workflows and improve human ... Support scientific research projects and develop geospatial algorithms while collaborating among ...

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Data Scientist Research Analyst information

What does a research data scientist do?

A research data scientist analyzes large datasets to extract insights, develop models, and support decision-making. They often use statistical methods, machine learning tools, and programming languages like Python or R, working in research or development environments to solve complex problems. Strong analytical skills and knowledge of data management are essential for this role.

What are Data Scientist Research Analysts?

Data Scientist Research Analysts are professionals who use statistical, analytical, and computational techniques to extract insights from data and support business or research decisions. They combine skills in data science, such as machine learning and programming, with research analysis methods to interpret complex datasets, identify patterns, and generate actionable recommendations. Their work often involves collecting, cleaning, and analyzing data, building predictive models, and communicating findings to stakeholders. Data Scientist Research Analysts work in a variety of industries, including finance, healthcare, technology, and government.

How do Data Scientist Research Analysts typically collaborate with other teams within an organization?

Data Scientist Research Analysts often work closely with cross-functional teams such as engineering, product management, and business strategy. They translate complex data findings into actionable insights that inform decision-making across departments. Collaboration may involve regular meetings to align on project goals, sharing data visualizations, and communicating technical results to non-technical stakeholders. This collaborative environment helps ensure that data-driven solutions are both technically robust and aligned with organizational objectives.

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

AspectData Scientist Research AnalystData Analyst
Required CredentialsBachelor's or Master's in Data Science, Statistics, or related fieldsBachelor's degree in Statistics, Mathematics, or related fields
Work EnvironmentResearch-focused, often in tech, finance, or healthcare industriesBusiness or corporate settings, supporting decision-making
Employer & Industry UsageResearch institutions, tech companies, large corporationsRetail, finance, marketing, and other industries
Common Search & ComparisonOften compared for data analysis and research rolesMore general data analysis roles

The main difference is that Data Scientist Research Analysts focus on advanced research, modeling, and predictive analytics, often requiring higher technical skills and specialized education. Data Analysts typically handle data cleaning, reporting, and basic analysis to support business decisions. Both roles overlap in data handling but differ in complexity and scope.

What are the key skills and qualifications needed to thrive as a Data Scientist Research Analyst, and why are they important?

To thrive as a Data Scientist Research Analyst, you need strong analytical skills, statistical knowledge, and proficiency in programming languages such as Python or R, usually supported by a degree in data science, statistics, or a related field. Familiarity with data visualization tools (like Tableau or Power BI), machine learning frameworks, and experience with databases (SQL) are typically required. Critical thinking, problem-solving abilities, and effective communication are essential soft skills for transforming complex data into actionable insights. These skills are crucial for generating valuable research outcomes, supporting business decisions, and effectively conveying data-driven findings to stakeholders.

Can a data scientist do the job of a data analyst?

A data scientist can often perform the tasks of a data analyst, as both roles involve analyzing data to extract insights. However, data scientists typically have more advanced skills in machine learning, statistical modeling, and programming, which may go beyond the scope of a data analyst's responsibilities. The roles can overlap, but the specific job requirements depend on the organization and project needs.

Is 40 too late for data science?

Data Scientist Research Analysts can enter the field at any age, as success depends on skills, experience, and continuous learning. Many professionals transition into data science later in their careers by acquiring relevant knowledge in programming, statistics, and tools like Python or R. Age is less important than demonstrated expertise and the ability to adapt to evolving technologies.

What is the 80 20 rule in data science?

In data science, the 80/20 rule, also known as the Pareto principle, suggests that roughly 80% of the results come from 20% of the efforts or data. Data scientists often use this concept to focus on the most impactful features, data subsets, or models to improve efficiency and outcomes.
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Data Scientist Research Analyst jobs near you
Infographic showing various Data Scientist Research Analyst job openings in the United States as of July 2026, with employment types broken down into 89% Full Time, 6% Part Time, 1% Temporary, and 4% Contract. Highlights an 83% Physical, 7% Hybrid, and 10% Remote job distribution.

Data Scientist (Research & Evaluation)

AJAIA

Fort Lauderdale, FL • On-site

Full-time

Posted 26 days ago

Job description

Role Overview
LaunchEd is an innovation platform that helps education companies prove the real-world impact of their products through independent research and evidence-based validation. We partner with promising edtech companies, evaluate their effectiveness, and use those insights to support investment, growth, and go-to-market decisions.
As the Data Scientist (Research & Evaluation), you'll build and deliver the research behind those decisions. You'll design studies, analyze quantitative data, measure product efficacy and return on investment (ROI), and produce clear, defensible findings that help determine whether products are creating meaningful outcomes for schools, educators, and students.
This role is ideal for someone who enjoys statistical analysis, research design, and using data to answer important business and educational questions.
Key Responsibilities
Efficacy & ROI Evaluation
• Baseline statistical benchmarks at intake and track them throughout program participation, from
intake to graduation.
• Design and run studies measuring product impact on student and operational outcomes.
• Quantify return on investment for companies and products across the LaunchEd portfolio.
• Produce independent, evidence-based evaluations that hold up to outside scrutiny.
• Produce efficacy and ROI reports on LaunchEd clients that are benchmarked against comparable
external data.
• Collaborate with higher-education and university partners to pursue university-sanctioned
research.
The LaunchEd Validation Score
• Develop and maintain the LaunchEd Validation Score as a core piece of platform IP.
• Establish a consistent, defensible methodology that can serve as a credible market standard for
edtech validation.
• Translate study findings into the standardized LaunchEd Validation Report.
In-House Study Capacity
• Deliver multiple efficacy and ROI studies per year as a repeatable in-house capability.
• Reduce reliance on external efficacy and validation vendors.
Cross-Department Support
• Provide research and analysis support across LaunchEd teams, including network grading and
accreditation.
• Partner with the deal team to feed validated outcomes into Gate 2 reviews and Investment
Committee decisions.
Requirements
• Strong quantitative and statistical analysis skills, with the ability to design and run studies
independently.
• Experience measuring efficacy, outcomes, or return on investment, ideally in education or a
related field.
• Ability to build clear, defensible methodologies and translate data into decision-ready findings.
• Familiarity with education data, student outcomes, and relevant compliance considerations such
as FERPA and COPPA is a plus.
• Proficiency with data analysis tools such as Excel, SQL, and Python or R, and clear data
visualization.
• Detail orientation, independence, and discretion handling confidential company and student data.
• Alignment with LaunchEd's mission and the Strong Minds, Good Hearts ethos.
Benefits
  • Competitive base salary + performance incentives
  • Professional development opportunities

Apply