Applied Data Analytics Jobs (NOW HIRING)

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.

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.

• 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

• 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

• 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

• 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

• 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

• 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

• Python (pandas, NumPy, scipy, Jupyter)
• SQL
• LLM APIs (Claude, GPT)
• Git and version control

• Databricks
• Cloud storage
• Knowledge graph concepts