Staff/Senior Applied Data Scientist – Research

This is a fully remote position, open to applicants in India.

📋 Description

• Collaborate in the development of scoring frameworks and metrics models, aiding in signal selection, weighting logic, and model architecture across various GTM insight types (acquisition, expansion, retention, strategic).

• Create prototype insight logic using Python notebooks: gathering features from HG's structured data resources, implementing model components, and conducting stress tests on outputs.

• Design and execute validation experiments to ensure that insight outputs are directionally accurate, well-calibrated, and meaningful across the entire vendor landscape.

• Assist in the design of ontology and entities, considering how vendors, products, companies, and relationships should be organized to enhance specific insights, informed by a conceptual grasp of the knowledge graph schema.

• Convert insight designs into clear, production-ready implementation briefs.

• Accurately document model specifications: defining components, feature engineering, aggregation logic, handling edge cases, and outlining expected output distributions.

• Engage in handoff reviews with the production team, addressing implementation inquiries and refining specifications based on feedback regarding feasibility.

• Contribute to the prioritized insights catalog by researching new insight concepts, evaluating data availability, and framing feasibility.

• Remain updated on GTM data science methodologies, competitive intelligence techniques, and relevant analytical methods that could enhance the insight library.

⛳️ Requirements

• Depth in statistical modeling: Ability to design and implement a variety of scoring and metrics models from foundational principles; comfortable with component weighting, normalization, signed rate-of-change metrics, composite aggregation, and distribution analysis; understands when a technique is suitable and why.

• Proficiency in Python for analytical prototyping: Strong skills in notebook-based Python for data manipulation, feature development, model prototyping, and output validation; familiar with pandas, NumPy, and Scikit as daily tools.

• SQL expertise: Skilled in querying structured data at scale; utilized for signal extraction, feature derivation, and validation checks across extensive vendor and company datasets.

• Analytical rigor and validation mindset: Capable of critically assessing whether a model measures what it claims; designs validation experiments, examines edge cases, and identifies when outputs fail sanity checks.

• Clear technical communication: Able to articulate analytical logic into precise written specifications; the production brief is a crucial deliverable.

• Experience with LLM APIs: Practical experience using Claude, GPT, or similar APIs as an effective tool; can create efficient prompts, incorporate LLM steps into an analytical workflow, and critically evaluate output quality.

• Understanding of knowledge graph concepts: Conceptual knowledge of how entities, relationships, and attributes are organized within a graph; capable of reasoning about how graph-derived features (e.g., vendor-product-company traversals) should inform insight design, without necessarily writing production Cypher.

• Experience in GTM/Management Consulting or IT Research, familiar with concepts like install base, intent signals, competitive intelligence, and market analysis. Experience with writing Cypher or querying graph-structured data directly.

• Proven ability to work collaboratively with engineering, product, and GTM teams.

• Background in a B2B SaaS or data products setting.

🏝️ Benefits

• Competitive salary

• Flexible working hours

• Professional development budget

• Home office setup allowance

• Global team events