• Design, build, and scale advanced detection and diagnostic models for solar and storage assets. • Enable faster detection, fewer truck rolls, and better customer experience. • Report to the Director of Data Science and partner closely across product management, engineering, and operations to take models from idea to production in Omnidian’s Resolv platform.

• Conduct data discovery and exploratory data analysis across structured and unstructured data • Perform data cleansing, normalization, aggregation, and modeling • Design and build analytic applications in Palantir Foundry, including dashboards and interactive workflows • Apply advanced analytics and statistical methods such as regression, clustering, hypothesis testing, and A/B testing • Develop and embed AI/ML capabilities (LLMs, NLP, RAG/GraphRAG) into analytic workflows • Create clear visualizations and metrics to support executive and operational decision‑making • Collaborate in an Agile/Scrum environment to rapidly iterate on solutions • Communicate insights effectively to both technical and non‑technical stakeholders

• Lead data management for complex, global clinical trials from study setup to database lock • Serve as the main client contact for all data management deliverables • Oversee SAE, PK, and external data reconciliation and database deployment • Manage timelines, budgets, and resource allocation to meet customer expectations • Mentor junior data management staff and promote best practices • Drive process improvements and ensure regulatory and SOP compliance

• Work directly with stakeholders (engineers, program managers, subject matter experts) to scope problems, formulate the right analytical questions, and translate business challenges into rigorous data science approaches • Apply deep analytical thinking to decompose complex problems—critically evaluate data quality and relevance, challenge assumptions, and design methods that address the business need • Develop statistical models, machine learning solutions, and analytical frameworks that deliver actionable insights and drive operational decisions • Build user-friendly, production-grade ML/AI applications (e.g., Streamlit, Dash) and analytical artifacts that provide data insights to teams across the enterprise and enable better decision making • Write production-quality Python code and develop analytical pipelines using cloud-based platforms (AWS, Databricks) to support scalable and reproducible data science workflows • Deliver insights through multiple channels—executive recommendations, analytical reports, interactive dashboards, and direct consultation with business leaders • Take ownership of the technical quality and business impact of your work—make thoughtful methodological decisions, validate approaches rigorously, and stand behind your recommendations • Stay current on analytical methods, statistical techniques, and domain-specific best practices to continuously improve the quality and impact of your work