• Leading a team of 30+ (and growing!) data scientists, ML Ops, and data engineers supporting Lilly's US Sales & Marketing organization. We drive critical business decisions and impact using statistics and AI/ML models.• Close partnership with marketing, technology, and executive leadership to translate business problems into analytics insights and capabilities for 20+ brands and $1B in annual spend.• Team capabilities: Marketing Mix Models, ROI and impact measurement, OPEX optimization, sales forecasting, customer segmentation, designed experiments, predictive analytics, AI/ML-driven targeting.

• Contributing developer to an in-house Geographical Hierarchical Bayesian Marketing Mix Model (GHBMMM) solution implemented in Tensorflow.• Partnered with Ads product teams to drive pricing decisions using statistical models and novel data sources.• Methodology: bayesian mixed models, ROI and impact measurement, A/B tests and designed experiments, causal inference, optimization.

• Team lead for 6 US and 7 India-based data scientists driving LillyUSA’s advanced analytics capabilities. Fostered a team culture emphasizing technical excellence and business impact.• Led hiring, on-boarding, and mentoring for new US and India team members (12+). Developed a standardized learning curriculum and prepared performance reviews/nominations.• Partnered with marketing leadership to translate business problems into analytics projects that impacted $1B in annual spend. Team deliverables: ROI and impact measurement, revenue optimization, sales forecasting, customer segmentation & targeting, experiments.• In addition to leadership responsibilities, developed and delivered analytics projects to drive launch brand strategy, omnichannel impact measurement, sales forecasts, ect.

• Delivered quarterly Marketing Mix Models measuring ROIs for 17 brands and $500M+ in annual spend. Led development of an automated modeling pipeline and dashboard to democ- ratize insights directly to leadership (2022 CIDO award winner).• Designed, executed, and analyzed marketing experiments costing $80M across DTC, digital, and doctor-level marketing. Insights led to $40M+ in savings and added revenue.• Built predictive models using XGBoost to dynamically identify high-value doctors, improving sales targeting by 20%+ over existing approaches.• Methodology: machine learning, regression, predictive models, causal inference, matching, designed experiments, optimization, mixed and generalized linear models, clustering.

- Developed a new data-driven model for variability of inhaled/nasal products; designed andconducted a comprehensive SAS simulation study of the FDA's PBE approach- Led weekly teleconference for sn industry working group; prepared technical reports and manuscriptsfor publication; presented internal IPAC-RS webinar

Supported industrial and national security projects using functional data methods and data visualization, including presentations and written reports for customers

Field and lab-based research on dietary requirements and food selection by zoo-based golden snub-nosed monkeys.

Held informational sessions and open advising hours for prospective students. Assisted with administrative tasks such as organizing and staffing events, filing student records, and communicating with students.

Lab assistant interested in phytolith identification and the application of phytoliths and carbon isotopes to the reconstruction of past ecosystems. Research projects resulted in two peer-reviewed publications and two oral presentations at Geological Society of America annual conferences.