• Lead & scale Product Analytics • Lead and mentor a team of Product Analysts, helping them grow technically and strategically • Own the Product Analytics roadmap, priorities, and delivery • Establish best practices for product analytics, experimentation, and decision-making • Ensure high-quality, actionable insights that influence product and business outcomes • Partner closely with Product Managers to define success metrics, KPIs, and north-star metrics • Translate business and product questions into analytical frameworks and insights • Proactively identify opportunities, risks, and areas for product improvement • Support discovery, experimentation, and post-launch analysis • Act as the primary analytics partner for Product and a close collaborator with Data and ML teams • Align analytics efforts with the broader AI strategy and company goals • Work with Data Engineering to define data requirements, tracking plans, and data models • Play a key role in shaping the analytics foundation for SweedAI • Help define how data and insights power AI-driven product capabilities • Collaborate with ML engineers on feature definition, evaluation metrics, and model monitoring • Ensure analytics is embedded into AI-powered workflows and decision systems

• Build modern data pipelines to process public budget data • Design and maintain classification pipelines for budget documents • Implement data quality standards across fiscal map products • Analyze public fiscal data and collaborate with various teams

• The Lead Data Scientist is responsible for leading data science initiatives that drive business profitability, increased efficiencies and improved customer experience. • This role assists in the development of the Home Depot advanced analytics infrastructure that informs decision making by applying expertise of both business and Advanced Analytics Modeling techniques. • Lead Data Scientists focus on seeking out business opportunities to leverage data science as a competitive advantage. • This role has expertise in one or more data science specializations, such as optimization, computer vision, recommendation, search or NLP. • Responsible for large data science projects, identifying opportunities to leverage best technology and approach, and mentoring data scientists on the project team. • Expected to own the library of reusable algorithms for future use, ensuring developed codes are documented. • Supports the building of skilled and talented data science teams by providing input to staffing needs and participating in the recruiting and hiring process. • Leads data science communities across several business units.

• Lead the design and development of ML systems that solve complex, ambiguous business problems • Make sound technical decisions on model architecture, evaluation methodology, and tradeoffs • Set standards for model validation, testing, and monitoring across the team • Identify when "good enough" is appropriate vs. when deeper investment is warranted • Debug and troubleshoot models that fail in production - understand why they fail, not just that they fail • Frame business problems as well-defined ML tasks with clear success criteria • Build robust predictive models (classification, regression, time series, causal inference) • Implement rigorous train/validation/test methodology to ensure real-world generalization • Identify and prevent data leakage, overfitting, and other failure modes before they reach production • Define metrics that align model performance with actual business outcomes • Conduct holdout testing on true out-of-sample data - recognize when CV metrics are misleading • Design and analyze experiments to measure causal impact • Communicate model limitations, uncertainty, and risk to technical and non-technical stakeholders • Partner with product, engineering, and business teams to ensure ML solutions solve real problems • Translate complex technical concepts into actionable recommendations for stakeholders • Contribute to hiring and technical interviews