• Lead work to determine what observations are needed to improve aerosol microphysical models and design decision-relevant outdoor experiments. • Develop an inverse modeling framework to use SABRE, AToM, and other relevant in situ observational datasets to improve existing aerosol microphysical models, potentially including adjoint-based approaches. • Design and run data denial experiments to determine which observations are most important for constraining microphysical parameters to help define a minimum viable instrument suite for a future outdoor field experiment. • Develop formal Observing System Simulation Experiments (OSSEs) that simulate observations of an aerosol plume under different potential instrument suites, sampling strategies, and cadences to quantify the marginal value of different measurement strategies. • Repeat OSSE analyses across a range of plume conditions, atmospheric states, and experimental configurations, and translate OSSE results into practical field campaign recommendations: where to sample, how often, at what altitude, with which instruments, and with what acceptable error bounds. • Build data pipelines and processing workflows for future field experiment data, ensuring that data can be rapidly quality-controlled, analyzed, and used to update model parameterizations. • Work closely with Reflective’s science, engineering, and data teams to translate model uncertainty into concrete observational requirements. • Write scientific papers, concise memos, technical documentation, and public-facing summaries that make what has been learned, what remains uncertain, and how the results should inform experiment design  clear to funders, policymakers, researchers, and the wider field.

• Leverage Doximity's extensive datasets to optimize, tune and evaluate AI products for medical professionals on our platform. • Play a key role in creating both product and client-facing analytics. • Inform data team strategy by working with the product leaders and managers. Actively participate in execution and some planning of organizational data team strategy. • Collaborate with a team of product managers, analysts, and other developers to define and lead data projects from data ingestion to analysis to recommendations.

• Support defining the HR Analytic roadmap and toolsets required including system and implementation strategy. • Build automated and scalable reporting & analytic frameworks to improve accessibility to data and supporting decision making. • Ensure data quality, governance, and integrity across all HR data sources. • Support advanced analytic initiatives such as predictive modeling, workforce planning and talent insights. • Support Business Process optimization efforts to align with clear HR data governance. • Partner with HR Business Partners, COEs and leadership teams to identify analytics needs and proposed solutions. • Support Operational Reporting requests as necessary.

• Develop causal estimates for long-term impact of short-term metric movements. • Build frameworks for estimating how the impact of product changes evolves over time. • Create frameworks and tooling to evaluate the heterogeneous impact of product changes • Develop and apply causal inference methods, including experimental, econometric regressions, and quasi-experimental methods to measure a wide-range of platform/product impacts.