Staff/Senior Applied Data Scientist – Research
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Job Description
Staff/Senior Applied Data Scientist collaborating on GTM insights and analytics at HG Insights. Designing statistical models and validating data logic for their Contextual Intelligence initiative.
Responsibilities:
- 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.
Requirements:
- 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.
Benefits:
- Competitive salary
- Flexible working hours
- Professional development budget
- Home office setup allowance
- Global team events
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