• acquire, clean and develop datasets from internal and external sources to support business analysis and reporting • acquire, validate, store, protect, and adhere to processes around data to ensure the accessibility, reliability, and timeliness of the data for its users • develop and execute data architectures, policies, practices and procedures that properly manage the full data lifecycle needs of an enterprise • interact with business and IT departments in understanding user needs • translate business needs to technical requirements in terms of data needs • manage multiple priorities and meet deadlines • maintain confidentiality and work independently • identify opportunities for process improvements • coordinate with other teams to ensure all aspects of data needs are taken into account • coordination and completion of projects on time and within scope • prepare data for reporting and analysis including visualizations to support business requirements • perform in-depth data analysis to derive insights that drives business outcomes and decisions • exhibit strong written and verbal communication skills • be an innovative thinker with an innate curiosity around data • experience in problem analysis and process documentation

• Own the full data platform — the warehouse, streaming and batch ingestion, the semantic layer, and BI infrastructure. • Define and enforce data governance — access management, quality standards, and a well-modeled, trustworthy "gold" layer. • Make the warehouse the source of truth for operational, product, and financial analytics. • Build the models that power delivery intelligence — forecasting, prediction, optimization, matching/allocation, and reliability scoring that turn our data into better real-time decisions. • Stand up the ML systems that make this real and reliable: feature pipelines, training, serving, evaluation, and drift monitoring. • Design the learning loop — instrument decisions and outcomes so models continuously improve. • Drive the AI agent initiative to production, partnering with product and engineering on the surrounding workflows. • Manage and grow the data and ML team (starting from a small core) — hire data engineers, analytics engineers, and data scientists/ML engineers as the platform matures. • Establish a clear intake process for ad hoc data requests so the team works from a roadmap, not in reactive mode. • Partner with Sales, Customer Success, and Finance on data-driven analyses and decisions.

• Work with clients looking to break into or grow within data science roles • Teach core data science strategy, machine learning, and predictive modeling skills • Help clients strengthen data cleaning and preprocessing, data visualization, statistical analysis, and data pipeline automation practices • Guide clients on applying emerging tools like AI fundamentals, AI model evaluation, and AI automation • Offer resume review, LinkedIn review, and cover letter guidance • Conduct behavioral and technical interview prep tailored to data science roles • Provide networking strategy, salary negotiation, promotion strategy, broader skill building, and support for those pursuing freelancing opportunities

• Lead analytics with AAD’s largest end users • Develop analytics dashboards and data structures • Work collaboratively with internal and external customers in support of the RepairStack™ platform • Collaborate with internal and external stakeholders in analytics & visualization development/enhancement • Improve & standardize data management and data collection processes in support of analytics & visualizations • Troubleshoot and resolve issues by working directly with customers, third‑party partners, and internal teams • Build robust documentation in support of analytics and reporting structures • Support alignment of key financial accruals tied to CIP rebates to ensure financial accuracy and compliance.