This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We're looking for a Senior Data Scientist with deep computer vision and ML expertise to join our document verification team. You'll own ML systems that analyze identity documents at scale — from model design through production — while also serving as a trusted technical advisor to customers. This role blends hands-on engineering with real customer impact. - Design, build, and own deep learning models — including CNNs and transformer-based vision and multimodal architectures — for document classification, fraud detection, image quality assessment, field extraction, and authenticity checks. - Own ML solutions end to end: data analysis, model training, deployment, monitoring, and continuous improvement in production. - Lead technical deep dives with customers, explaining model behavior, performance metrics, and tradeoffs in ways that are clear and actionable. - Translate customer, regulatory, and business requirements into modeling objectives, and communicate results effectively to both technical and non-technical audiences. - Partner with engineering and product teams to deliver scalable, reliable document verification systems. Qualifications - 8+ years of hands-on experience in computer vision, or a Bachelor's in Computer Science (or related field) with equivalent professional depth. - Proven track record designing, building, and maintaining production ML systems. - Strong written and verbal communication skills, including experience in customer-facing technical roles. - The ability to translate technical details into business-facing narratives — not just the "what," but the "why." - Comfort balancing technical rigor with customer, business, and compliance needs. Requirements - Master's or Ph.D. in Computer Science, Machine Learning, Computer Vision, or a related field. - Experience with document verification, OCR, identity, fraud detection, or image forensics. - Familiarity with transformer-based vision models, multimodal systems, or LLMs. - Experience working with enterprise customers or in regulated environments.

• Lead the development of predictive models to identify members at risk of care gaps and prioritize outreach opportunities • Perform advanced statistical analyses to uncover drivers of HEDIS performance variation • Develop segmentation strategies (provider, population, geography) to target high-impact interventions • Design forecasting models to project HEDIS performance and simulate intervention scenarios • Translate complex analytical findings into clear, actionable recommendations for executive and operational stakeholders • Identify the highest-value opportunities for improvement based on impact, feasibility, and ROI • Establish frameworks to measure the effectiveness of HEDIS interventions and initiatives • Conduct pre/post and longitudinal analyses to evaluate program impact • Quantify financial and quality outcomes, including Stars impact, incentive revenue, and cost of care implications • Design and oversee enterprise HEDIS performance dashboards and reporting tools • Collaborate with Clinical, Quality, Provider Engagement, and Operations teams to align analytics with business needs

• The Marketing Analytics Lead builds, maintains, and operationalizes a trusted marketing data foundation that powers reporting, insights, and AI-ready analytics. • Designs and manages scalable ETL/ELT pipelines and analytics-ready data models across the marketing ecosystem—CRM, marketing automation, paid media, web analytics, and messaging platforms—ensuring data is accurate, governed, and accessible for full-funnel measurement. • Designs, develops, and maintains reliable ETL/ELT pipelines that ingest and unify marketing data from multiple systems. • Builds and optimizes analytics data stores and curated, analytics‑ready datasets that support scalable reporting, self‑service analysis, and standardized full‑funnel measurement. • Implements and maintains standardized definitions, keys, and documentation to ensure consistent interpretation, data quality, and downstream usability. • Builds and maintains dashboards and recurring performance reporting that supports marketing leadership, demand generation, and sales alignment—focused on business outcomes. • Partners with the Marketing Analytics Director to deliver insights and recommendations by summarizing trends, identifying drivers, and highlighting opportunities for optimization across channels and funnel stages. • Monitors and troubleshoots pipelines and integrations; proactively identifies failure points, resolves incidents, and improves resiliency through alerting, validation checks, and documented recovery steps. • Maintains detailed runbooks and operational documentation to support continuity, scalability, and onboarding.

• Lead discovery and solution design for GenAI use cases, translating business problems into concrete architectures (LLM decision, RAGs, fine‑tuning, agents, guardrails) • Build end‑to‑end GenAI applications: data ingestion, retrieval layer, orchestration (e.g. LangChain/LlamaIndex/LangGraph), API/backend, and simple UI where needed. • Design and implement RAG pipelines with vector databases, hybrid search, rerankers, query transformation, and evaluation frameworks for relevance and robustness. • Perform model selection, prompting strategies, and fine‑tuning (LoRA/QLoRA/SFT) for text, code, and multimodal models, including evaluation and A/B testing. • Implement safety, compliance, and governance controls (input/output filters, PII handling, audit logs, human‑in‑the‑loop review where required). • Collaborate with data engineers, product owners, and full‑stack developers on scalable architectures, SLAs, and integration with existing enterprise systems • Gather technical requirements and estimate planned work. • Mentor other data scientists/engineers in GenAI patterns, code quality, and best practices; contribute to internal libraries, templates, and reusable components. • Stay current with GenAI landscape (new open and hosted models, agentic frameworks, evaluation techniques) and perform targeted PoCs to validate them.