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

• Support the delivery of AI and ML solutions across fast-growing Workday products • Collaborate with senior colleagues to learn and apply data science techniques on live client projects • Build your technical skills in a fast-paced environment while contributing meaningfully to the team's delivery and growth

• You will be responsible for developing high quality AI and ML solutions for our fast growing suite of Workday products – including Kainos Smart (Smart Test, Smart Audit and Smart Shield), Employee Document Management and Pay Transparency Analyzer. • You will work on the design and delivery of advanced AI/ML solutions that improve the functionality, scalability and efficiency of our Workday product suite. • You may also carry some formal line management responsibilities, including appraisals, for more junior members of the team.

Role Description Client has developed a prototype over the last few months for a new QMS and they are leveraging heavy AI and automation tools. One of our current consultants is leaving, but this isn't technically a backfill for that profile, since they are in a different spot now current state than they were 4 months ago when our contractor began. Qualifications - Master's degree in a quantitative field with 7-10+ years of hands-on experience applying AI/ML to medical device manufacturing, quality, and post-market data with demonstrated, measurable impact. - Deep expertise designing and deploying agentic AI systems that autonomously reason across manufacturing, quality, and post-market datasets, execute multi-step analysis, self-correct, and drive decisions with minimal human intervention. - Proven, production-grade experience using Claude LLMs (Claude 3.5+) for regulated use cases, including prompt orchestration, tool calling, structured output generation, guardrails, and audit-ready logging. - Strong expertise converting unstructured medical device data (complaints, CAPAs, investigation reports, service notes, operator logs, SOPs, PDFs, emails) into structured, schema-aligned datasets suitable for analytics, modeling, and regulatory review. - Experience building AI pipelines for entity extraction, event classification, failure mode normalization, trend tagging, risk categorization, and summarization aligned to manufacturing and quality taxonomies. - Strong foundation in predictive modeling, clustering, time-series analysis, anomaly detection, and statistical inference applied to process parameters, yield, defects, equipment signals, and failure trends. - Advanced proficiency with Databricks (Spark, SQL, Delta Lake), Python, and SQL to ingest, structure, and analyze large-scale manufacturing, quality, and post-market datasets across millions of records. - Demonstrated ability to correlate defects, NCRs, CAPAs, complaints, and service events with upstream manufacturing signals and process changes using data-driven root cause methodologies. - Hands-on experience extracting and analyzing data from SAP Tahiti, Salesforce, TrackWise and QMS data while maintaining data integrity, traceability, and compliance in regulated environments. - Track record of deploying AI systems that reduce investigation cycle time, improve defect detection, automate failure analysis, and deliver clear, defensible insights for manufacturing, quality, regulatory, and leadership teams. Company Description