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Data Scientist Research Analyst Jobs (NOW HIRING)

HG Insights

Applied Data Scientist - Research

Role Overview The Applied Data Scientist - Research is a collaborative analytical partner to the Head of Data Science, contributing to the design and validation of GTM insights that power the ...

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Collaborate with researchers, analysts, and leadership teams supporting environmental and public health initiatives Minimum Qualifications * Bachelor's degree in Data Science, Computer Science ...

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Data Analytics & Computational Sciences Job Sub Function: Data Science Job Category: Scientific ... The Principal Data Scientist, R&D Oncology will support how we advance data capture, build and ...

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Principal Data Scientist, R&D Oncology

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Data Analytics & Computational Sciences Job Sub Function: Data Science Job Category: Scientific ... The Principal Data Scientist, R&D Oncology will support how we advance data capture, build and ...

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Data Scientist Research Analyst information

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 standard data analyst responsibilities. The roles can overlap, but the specific job requirements depend on the organization and project needs.

Is 40 too late for data science?

A Data Scientist Research Analyst can enter the field at age 40 or older, as experience, skills, and continuous learning are valued. Many professionals successfully transition into data science later in their careers by acquiring relevant skills such as programming, statistics, and tools like Python or R, and building a strong portfolio or certifications. Age is less important than demonstrated expertise and ongoing development in the field.

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 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.

What is a data research analyst's salary?

A data research analyst's salary typically ranges from $50,000 to $85,000 annually, depending on experience, education, and location. Entry-level positions may start lower, while experienced analysts with advanced skills in statistical software and data visualization tools can earn higher salaries.
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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 June 2026, with employment types broken down into 2% Locum Tenens, 33% Full Time, 10% Part Time, 51% Contract, and 4% Nights. Highlights an 89% Physical, 3% Hybrid, and 8% Remote job distribution.
Applied Data Scientist - Research

Applied Data Scientist - Research

HG Insights

Remote

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

About HG Insights
HG Insights is the pioneer of Revenue Growth Intelligence. For more than a decade, we have delivered comprehensive, AI-driven datasets on B2B buyers, technology adoption, IT spend, and buyer intent, sourced from billions of data points. Today, we are a trusted partner to Fortune 500 technology companies, hyperscalers, and innovative B2B vendors seeking precise go-to-market analytics and decision-making.
Through an evolving suite of AI agents that incorporate first-party data and buyer signals, HG Insights enables AI-powered GTM automation across sales, marketing, RevOps, and data analytics teams, modernizing GTM execution from strategy through activation.
Role Overview
The Applied Data Scientist - Research is a collaborative analytical partner to the Head of Data Science, contributing to the design and validation of GTM insights that power the Contextual Intelligence initiative.
You will co-develop insight logic, selecting signals, designing scoring frameworks, prototyping models in Python, and validating outputs. You will also contribute to the production-ready briefs that are implemented in the data production pipeline by the engineering team.
This role sits at the intersection of statistical modeling, structured data analysis, and applied AI. You are comfortable reasoning about how to measure something rigorously, how entities and relationships in a knowledge graph can be leveraged, and how to use LLMs as a practical tool in the insight development workflow, not as a subject of research, but as part of the toolkit.
What You Will Do
Insight & Model Development
  • Co-develop scoring frameworks and metrics models, contributing to signal selection, weighting logic, and model structure across a range of GTM insight types (acquisition,expansion, retention, strategic)
  • Prototype insight logic in Python notebooks: assembling features from HG's structured data assets, implementing model components, and stress-testing outputs.
  • Design and run validation experiments to confirm that insight outputs are directionally correct, well-calibrated, and meaningful across the full vendor universe
  • Contribute to ontology and entity design, thinking through how vendors, products, companies, and relationships should be structured to support a given insight, informed by a conceptual understanding of the knowledge graph schema
Production Brief Development
  • Translate insight designs into clear, implementation-ready production briefs
  • Document model specifications precisely: component definitions, feature engineering, aggregation logic, edge case handling, and expected output distributions
  • Participate in handoff reviews with the production function, answering implementation questions and refining specs based on feasibility feedback
Insight Research & Discovery
  • Contribute to the prioritized insights catalog, researching new insight ideas, assessing data availability, and framing feasibility
  • Stay current on GTM data science approaches, competitive intelligence methodologies, and relevant analytical techniques that could expand the insight library
What We're Looking For
Core Skills
  • Statistical modeling depth: Ability to design and implement a range of scoring and metrics models from first principles; comfortable with component weighting, normalization, signed rate-of-change metrics, composite aggregation, and distribution analysis; knows when a technique is appropriate and why
  • Python for analytical prototyping: Strong notebook-based Python for data manipulation, feature construction, model prototyping, and output validation; pandas, NumPy, and Scikit are daily
  • SQL: Proficient in querying structured data at scale; used for signal extraction, feature derivation, and validation checks across large vendor and company datasets
  • Analytical rigor & validation thinking: Ability to critically evaluate whether a model is measuring what it claims to measure; designs validation experiments, checks edge cases, and flags when outputs don't pass a sanity check
  • Clear technical communication: Able to translate analytical logic into precise written specifications; the production brief is a key deliverable
Applied AI & Graph Literacy
  • LLM API usage: Hands-on experience using Claude, GPT, or equivalent APIs as a practical tool; can design effective prompts, integrate LLM steps into an analytical workflow, and evaluate output quality critically
  • Knowledge graph concepts: Conceptual understanding of how entities, relationships, and properties are structured in a graph; able to reason about how graph-derived features (e.g., vendor-product-company traversals) should inform insight design, without necessarily writing production Cypher
Nice to Have
  • GTM/Management Consulting, or IT Research experience, familiarity with concepts like install base, intent signals, competitive intelligence, and market analysis. Experience writing Cypher or querying graph-structured data directly
  • Experience working collaboratively with engineering, product and GTM teams
  • Experience in a B2B SaaS or data products environment
Tools & Environment
Primary
  • Python (pandas, NumPy, scipy, Jupyter)
  • SQL
  • LLM APIs (Claude, GPT)
  • Git and version control
Working Knowledge
  • Databricks
  • Cloud storage
  • Knowledge graph concepts

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