• Serve as the primary analytics partner to HR and business leaders, understanding their challenges and translating them into analytical solutions. • Provide insights and recommendations that inform decisions on talent strategy, workforce planning, retention, and employee experience. • Build strong relationships with HRBPs, COEs, and leadership teams to ensure alignment on priorities. • Develop dashboards, reports, and analyses on workforce metrics (e.g., attrition, DEI, engagement, recruiting, performance). • Translate complex data into clear, actionable insights with strong storytelling and visualization. • Deliver executive-ready materials that connect people data to business outcomes. • Partner cross-functionally with analytics and technical teams to ensure data accuracy, resolve quality issues, and maintain consistent, reliable insights. • Use statistical analysis, predictive modeling, and trend forecasting to identify workforce risks and opportunities. • Guide stakeholders in building a data-driven culture within HR and across the business. • Drive adoption of self-service analytics platforms and democratize access to people insights.

• Design and implement scalable data pipelines to ingest, process, and transform large datasets (structured & unstructured). • Develop, validate, and optimize supervised and unsupervised machine learning models leveraging Python, SQL, and modern libraries. • Conduct feature engineering, model selection, and statistical modeling to deliver high-impact solutions. • Build and expose model APIs or containerized workflows for seamless integration and deployment in production environments. • Apply MLOps best practices to model versioning, testing, monitoring, and deployment. • Work with Big Data technologies such as Databricks and Snowflake to unlock analytics at scale. • Orchestrate complex workflows using tools like Airflow or Dagster for automation and reliability. • Collaborate with AI teams to refine prompt engineering and leverage AI tooling for model fine-tuning and augmentation. • Maintain familiarity with leading cloud platforms (AWS, Azure, GCP) for model training, deployment, and infrastructure management. • Partner with product, engineering, and business teams to translate requirements into technical solutions.

• Direct annotation by managing annotation budgets, sourcing data, and providing statistical analysis of annotations • Maintain and expand metrics used to evaluate agent performance and fairness • Create evaluation packages for Data Scientist and Machine Learning Engineers to use when running experiments • Ensure accurate, regular reporting on evaluation metrics to stakeholders • Manage periodic re-evaluation of production agents using user data and annotation

• Evaluate the effectiveness of new advertising channels • Set up and improve monitoring of statistical anomalies in metrics • Improve long‑term payback prediction models • Generate hypotheses to improve acquisition/product feeds/ad creatives/channel split/targets, etc. • Conduct data studies to uncover insights • Write ETL pipelines (data marts, alerts, and predictions) • Ask “why” and “what problem are we solving”