causal-inference jobs in San Diego

Staff Data Scientist leading analytics strategy for Intuit's Connected Accounts product, responsible for owning the analytics agenda end-to-end from account linking through engagement. The role requires applying ML and causal inference techniques to solve connected-accounts-specific problems, designing and running A/B tests and quasi-experimental designs, building member segmentation, and identifying AI/ML opportunities.…See more You will partner closely with Product, Engineering, and Design leadership on roadmap prioritization, mentor other data scientists, and use AI-native tools to synthesize analyses ranging from experiment readouts to churn deep dives.

Staff Quality Analyst role responsible for building dashboards and analytics that surface defect signals and drive leadership decision-making at scale. The role combines hands-on data analysis (SQL, Python, statistics) with AI-native workflows to accelerate defect detection and root cause analysis, plus structured change management to land measurement and process changes across large stakeholder groups.…See more Required skills include strong SQL/Python proficiency, applied statistics, experience with defect frameworks (DPMO), contact center operations familiarity, and demonstrated ability to communicate data findings credibly to executives and manage organizational change.

Senior Manager leading R&D strategy for digital health and AI/ML solutions in neuropsychiatry at a major pharmaceutical company. The role spans developing and validating digital endpoints, patient stratification approaches, and clinical trial optimization using advanced statistical modeling, multimodal data integration (digital phenotyping, speech/language analytics, imaging, biomarkers, omics), and longitudinal analysis.…See more Requires 6+ years in pharma/biotech analytics with demonstrated expertise in clinical development, digital health technologies, biostatistics, and machine learning; PhD or MD in a quantitative or clinical field preferred. Reports to senior leadership, partnering cross-functionally with clinical operations, regulatory affairs, and external collaborators to advance digital health standards and support regulatory qualification of novel endpoints.

As a Senior Data Scientist on PlayStation's Decision Science team, you will lead the design, execution, and interpretation of A/B tests and quasi-experiments to evaluate PS4-to-PS5 migration initiatives, growth marketing strategies, and campaign performance. You'll apply advanced causal inference methods, develop experimentation frameworks and best practices, and independently extract and analyze data using SQL and Python.…See more The role requires deep expertise in experimental design, statistical rigor, and the ability to communicate findings to drive business decisions across product, engineering, and marketing teams.

Lead research projects in AI/ML across areas including decision-focused AI, probabilistic modeling, causal inference, agentic systems, neuro-symbolic AI, and LLM-based reasoning. Define technical direction, mentor junior researchers, and translate research innovations into Intuit's products.…See more Requires a PhD, 8+ years of post-PhD research experience with top-tier publications, and deep expertise in one or more focus areas. Proficiency in Python, PyTorch, or TensorFlow, and strong fundamentals in deep learning, optimization, and statistical machine learning are essential.

Senior individual contributor responsible for end-to-end demand forecasting across Intuit's Global Business Solutions Group portfolio. Build and maintain predictive models incorporating time-series analysis, regression, causal inference, and ML techniques to translate business signals into accurate, decision-ready demand forecasts at multiple granularities.…See more Conduct A/B/n experiments to validate modeling choices, quantify forecast risk through scenario analysis, and act as trusted partner between technical teams and business stakeholders (Workforce Management, Capacity Planning, Finance, Marketing, Product) to ensure forecasts drive downstream capacity and expense planning. Continuously advance forecasting methodology, evaluate where AI/ML meaningfully improves accuracy, and support real-time in-season re-forecasting and analytics.