• Owning the end-to-end delivery of data science solutions, from problem framing through to production and ongoing improvement • Building advanced models, generative AI tools, and insight-driven products that go beyond what standard tools can achieve • Partnering closely with stakeholders to test ideas, demonstrate value early, and iterate based on real-world feedback • Working within agile squads and across domains, learning the business deeply and applying your expertise where it matters most • Contributing to a growing data science capability, sharing knowledge, lifting standards, and helping shape how we work

• Develop and maintain marketing ROI and analytics models • Analyze marketing and sales data to measure channel performance and advertising effectiveness • Partner with business stakeholders to address questions, review model outputs, and identify key business drivers behind marketing performance results • Work with Bayesian and linear models to evaluate marketing contribution and ROI • Support and improve Streamlit-based applications used for reporting and visualization • Build scalable Python-based data workflows and analytical tools • Export and manage reporting outputs using Excel and related formats • Collaborate with engineering and cross-functional teams to improve code quality and maintainability • Work within AWS environments including S3 and EC2 • Monitor applications and workflows using DataDog

• Ensure master data is AI‑ready, supporting machine learning, analytics, and Generative AI use cases. • Collaborate with Data Science and AI teams to: Define training‑data requirements, Prevent bias and duplication in AI inputs, Improve feature quality through high‑quality master data, Implement AI‑assisted data matching, classification, and enrichment where applicable. • Support explainability, lineage, and traceability requirements for AI and ML models. • Enable natural‑language and AI‑driven access to master data for analytics and decision support platforms. • Work closely with MDM platforms and tools. Define and oversee integration patterns between MDM, ERP, CRM, data platforms, and AI pipelines. • Collaborate with data engineering teams on cloud‑based data platforms, APIs, and automation workflows. • Ensure master data availability and consistency across operational, reporting, and AI systems. • Lead and mentor Master Data Stewards and Analysts. • Act as a key stakeholder in enterprise Data & AI initiatives. • Drive organizational adoption of data governance and AI‑driven data quality practices. Communicate MDM and AI value clearly to technical and non‑technical stakeholders.

• Design and build AI Solutions that use machine learning models, including GenAI, NLP, and predictive analytics solutions for healthcare applications • Develop, test, and deploy AI solutions in cloud and on-premise environments, ensuring reliability, scalability, and real-world impact • Work with large healthcare datasets, performing data preprocessing, feature engineering, and model training while ensuring compliance with HIPAA and other regulatory standards • Implement and optimize AI solutions within Gainwell’s existing technology stack, collaborating with engineering teams to ensure seamless integration • Continuously monitor, refine, and optimize AI solutions for accuracy, efficiency, and speed, leveraging MLOps best practices • Stay updated with the latest AI/ML advancements, exploring new technologies and methodologies to enhance solution effectiveness • Ensure AI implementations adhere to healthcare industry regulations, ethical AI principles, and data privacy standards • Identify opportunities to automate workflows and optimize business processes using AI-driven solutions