• Design, develop, and maintain scalable data pipelines using PySpark to efficiently process large volumes of data. • Develop, test, and optimize Machine Learning models for predictive analytics, including regression, classification, and clustering techniques. • Manage and deploy Machine Learning environments and workflows using AWS SageMaker, while leveraging Athena for data querying and analysis. • Integrate Machine Learning models into production pipelines, ensuring proper monitoring, maintenance, and continuous improvement through MLOps best practices. • Collaborate closely with cross-functional teams to understand business challenges and translate them into data-driven solutions. • Conduct advanced data analysis using Python and SQL to deliver actionable insights. • Work collaboratively with multidisciplinary teams using Git/GitHub and agile development methodologies.

• Develop and optimize machine learning models for recommendations and personalization • Build merchandising and product ranking models for eCommerce platforms • Design customer segmentation and scoring models within CRM systems • Translate business problems into analytical frameworks and solutions • Perform deep-dive analysis and create impactful data visualizations • Define and validate AI use cases with business stakeholders • Evaluate model performance using statistical and business metrics • Design and run A/B tests and experimentation frameworks • Monitor model performance and recommend optimizations • Collaborate with engineering teams to deploy and scale models • Work with marketing, eCommerce, and CRM teams to embed models into workflows • Communicate insights clearly to technical and non-technical stakeholders

• Assist the principal data scientist in developing and tuning predictive models (e.g., predicting equipment failure, estimating lead times, or optimizing inventory levels). • Assist in data validation, feature engineering, and quality checks on large-scale Army datasets to ensure model integrity. • Progressively develop expert-level skills in Palantir Foundry to build, update, and reuse predictive dashboards. • Help develop and update training materials, job aids, and technical documentation for predictive tools to ensure institutional adoption. • Support the reuse and long-term maintenance of analytics products and sustainment artifacts.

• Guidehouse is seeking a Director in our Health AI and Data Science practice to lead the delivery of data products, AI-enabled solutions, and scientific support that help healthcare leaders make better, faster decisions grounded in data. • In this role, you will own end-to-end client delivery across multiple accounts and capabilities — partnering with executives and product owners to translate priority decisions into clear use cases, governed metrics, and a roadmap of analytics, decision intelligence, and AI capabilities. • You will provide day-to-day delivery leadership (scope, schedule, staffing, risk, and quality), guide multidisciplinary teams (data science, data engineering, analytics, and UX/visualization), identify and support capture of new business, and ensure solutions are secure, scalable, and adopted in the client’s operating cadence. • Lead end-to-end delivery of multiple concurrent AI, data science (including qualitative/qualitative scientific support), and data product engagements, ensuring on-time, high-quality outcomes across public and commercial health workstreams, vendors, and client teams. • Co-lead scientific support service line to support hypothesis-driven research, statistical analyses, peer-review manuscript publication, and data-driven storytelling targeted to diverse audience personas (executives, community, etc.) • Partner with client executives and product owners to define decision-centric use cases and success measures, translating strategy into a prioritized roadmap of data products, analytics, and AI capabilities. • Translate executive decisions into clear product requirements and governance (KPI definitions, metric logic, decision thresholds, scenarios, and action triggers), ensuring outputs are trusted, repeatable, and decision-ready. • Direct analytics and model development across teams—framing hypotheses, guiding quantitative and qualitative methods, validating assumptions, and driving from root cause to opportunity sizing, prioritization, and quantified value cases. • Develop and quality-assure executive-ready deliverables (decision memos, briefs, and exhibits) that clearly communicate the “so what,” tradeoffs, and recommended actions for C-suite and senior government audiences. • Lead senior stakeholder engagement and delivery governance, running working sessions and executive readouts; synthesizing inputs; aligning decision-makers; managing risks and dependencies; and driving timely decisions across client and internal leadership. • Build, staff, and grow high-performing delivery teams through an apprenticeship model—coaching consultants through associate directors, setting clear expectations, and ensuring the right mix of functional, technical, and domain skills. • Drive business development and account expansion (with emphasis on federal health accounts), leading proposals and capture efforts; shaping solution approaches; and developing reusable delivery accelerators and data product assets. • Establish delivery standards for scalable, secure data products, including data quality practices, documentation, and responsible AI considerations appropriate for executive decision-making. • Own engagement financial and operational performance—managing scope, budgets, staffing, and margins; monitoring delivery health; and proactively mitigating risks to ensure sustainable, repeatable delivery.