• Develop predictive models for revenue and audience. • Perform advertiser segmentation based on data and behavior. • Build intelligent agents focused on business intelligence. • Develop and evolve solutions using Generative AI to optimize processes and support decision-making. • Collaborate with cross-functional teams to build data-driven products and solutions.

• Lead and oversee coding, CDI, charge capture, charge movement, and data quality operations across the organization. • Develop operational plans, annual goals, and performance metrics focused on coding productivity, accuracy, compliance, and documentation integrity. • Provide subject matter expertise in ICD-10-CM/PCS, CPT/HCPCS, DRGs, APR-DRGs, APCs, and related coding and billing practices. • Maintain accountability for coding quality, productivity, accounts receivable performance, and regulatory compliance outcomes. • Lead, train, and develop coding and CDI managers, lead-level staff, and team members while fostering a high-performing, engaged team culture. • Direct coding quality, CDI, and data integrity initiatives, including internal and external audit strategy, preparation, review, and response. • Partner with clinical, finance, revenue cycle, compliance, and operational leaders to improve coding accuracy, documentation integrity, and financial performance. • Oversee internal and external data reporting, analytics, and regulatory submissions while identifying trends and opportunities for performance improvement. • Ensure systems supporting coding and data quality functions are optimized, including encoder, abstracting, EHR, and reporting tools. • Support education for clinical and ancillary teams on documentation requirements, medical necessity, and accurate code assignment practices.

• Own end-to-end quality and delivery for critical Human Data projects while ensuring accuracy, consistency, and guideline adherence at scale. • Lead, coach, and grow a team of Team Leads and AI Tutors (full-time and contractors), including performance management, career development, and talent pipeline building. • Manage a globally distributed workforce spanning multiple employment types (full-time employees and contractors), fostering strong collaboration and consistent standards across time zones. • Collaborate closely with Members of Program Staff, Go-to-Market teams, and Engineering teams to understand model needs and translate them into clear data requirements and labeling strategies. • Define and continuously improve operational standards, annotation guidelines, taxonomy, and quality assurance processes. • Drive efficiency and scalability by identifying and resolving bottlenecks, implementing process improvements, and leveraging metrics/KPIs to track performance (e.g., send-back rate, tutor utilization, lead time). • Recruit, hire, and develop top talent while maintaining a high-performance, meritocratic culture with strong accountability. • Build and iterate training programs, certification processes, and onboarding systems for new team members and leaders. • Act as the voice of the tutors while connecting Team Leads with Human Data Engineers to drive continuous improvement. • Report on program status, risks, and outcomes to senior leadership while fostering cross-functional alignment.

Role Description Total Life is building a research program focused on suicide-risk detection, aiming to explore how symptom scores change over time and how this information can enhance near-term risk assessment. You would lead the statistical thinking behind this work — design and analysis, not just execution. Specifically: - Serve as co-investigator on a federally funded research application (biosketch and defined effort commitment required) - Design the statistical and validation plan: power/sample-size analysis for a rare-event outcome, model evaluation, discrimination and calibration, temporal validation - Advise on analytic methods for de-identified behavioral-health and claims data, including de-identification standards - Partner with our clinical and engineering teams to guide model development and interpret results responsibly Qualifications - PhD (or equivalent) in biostatistics, statistics, or a closely related field - Demonstrated experience building risk-prediction models, ideally with EHR, claims, or longitudinal healthcare data - Working knowledge of rare-event/class-imbalance methods and prediction-model reporting standards (e.g., TRIPOD) - Comfort with de-identified clinical data and methods such as HIPAA Expert Determination - US-based and able to be named on a US federal (DoD/NIH) research application - Prior experience on federally funded research (NIH/DoD) is a strong plus Engagement - Part-time / contract, remote - Compensation commensurate with experience and scope - Immediate start preferred given active research timelines, with potential to grow alongside our research program