• Coordinate 3–4 concurrent workstreams (data science, MLOps, engineering, front-end/back-end) and track progress against tight delivery timelines. • Align internal data science teams (Mexico, India, Spain, US) with partner S&T/engineering organizations. • Identify cross-functional blockers and drive proactive resolution to maintain schedule. • Represent the data science team in frequent S&T-led working sessions and executive updates. • Serve as primary liaison between data science teams, engineering/S&T teams and internal leadership. • Manage meeting cadence, follow-ups, execution tracking and action ownership. • Translate technical progress and risks into clear, concise status updates for stakeholders. • Ensure teams receive required data inputs, meet deliverables and coordinate dependencies. • Establish and manage workstreams, coordination processes and cross-team communication flows. • Drive accountability and orchestrate cross-team workflows without impeding execution velocity.

• 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.

• Diseñar y mantener pipelines de datos en plataformas cloud (GCP, AWS, Azure u otras) • Desarrollar y evaluar modelos de machine learning para casos de uso de producto • Experimentar con LLMs, embeddings y retrieval-augmented generation (RAG) • Construir prototipos y features de ML integrables a productos • Implementar, monitorear y versionar modelos en producción siguiendo buenas prácticas de MLOps • Colaborar con equipos de producto y desarrollo para llevar soluciones de IA a producción

• Review analytics across all divisions before they reach a client or a courtroom • Conduct detailed, cell-level quality control across models, workbooks, and projections • Define which data sources are used for which analysis types and hold the line on consistency • Own and maintain the firm's healthcare data library, including national and state-level datasets • Build documented, repeatable analytical frameworks for core engagement types • Establish standard operating procedures and review checkpoints • Provide structured direction and run disciplined review cycles with analysts • Identify practical improvements to tools, workflows, and automation