Role Description As Head of Data, you'll build and lead the company's data function while remaining deeply hands-on. This isn't a traditional management role; you'll personally analyze data, shape the technology stack, implement AI-driven workflows, and establish the analytical foundation that powers every strategic decision across the business. Working directly with leadership, you'll combine technical expertise, business acumen, and AI to build a lean, high-impact data organization capable of operating at startup speed. What You'll Do - Build and own the company's data function from the ground up - Audit and improve existing data infrastructure, tooling, and processes - Lead a lean data team while remaining hands-on with analysis - Leverage AI to automate workflows and increase team productivity - Analyze large datasets to answer strategic business questions - Deliver investment-grade analyses, dashboards, and executive briefings - Establish data governance, reporting standards, and analytical best practices - Partner with Product, Finance, Operations, and Leadership to drive business decisions - Continuously improve the company's AI-enabled data capabilities Qualifications - Experience in financial analysis, investment analysis, private equity, business analytics, or data-heavy roles within high-growth SaaS, fintech, or consumer technology companies - Strong hands-on experience working with large datasets - Excellent SQL and modern analytical tooling experience - Strategic mindset with strong commercial understanding - Experience implementing AI tools and AI-powered workflows - Outstanding communication and storytelling skills with executive stakeholders - Builder mentality with high ownership and execution focus - Based in the United States (Arizona preferred) Nice to Have - Consumer fintech experience - Startup or founder-led environment - Business Intelligence platforms - Python, SQL, or modern analytics stack - Experience building data functions from the ground up - Investment banking, consulting, or private equity background Benefits - Competitive salary plus meaningful equity - Flexible remote work - Health, dental, and vision insurance - Unlimited paid time off - High ownership with direct access to founders - Opportunity to build an AI-first data organization from day one - Collaborative, ambitious, and mission-driven culture - Build the AI-powered data function shaping the future of consumer financial wellness

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 - 7-10+ years of hands-on experience applying AI/ML to medical device manufacturing, quality, and post-market data with demonstrated, measurable impact Requirements - 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

• Lead the full AI product lifecycle: from identifying user pain points and defining KPIs to training foundation models and measuring real-world impact. • Design and execute rigorous experiments (A/B tests, causal inference) to validate new AI features and drive product optimizations. • Partner with Product Managers, Designers, and Engineers to translate business goals into a technical AI roadmap, ensuring our model development stays aligned with user needs and technical feasibility. • Build and maintain production-grade training pipelines and evaluation harnesses using reproducible, well-tested code. • Analyze production feedback and user interactions to iteratively refine models and maximize value. • Foster a collaborative culture: mentor teammates, simplify complex problems, and drive iterative, high-impact delivery.

• Design and implement scalable data architecture. • Develop ETL processes • Develop scripts in SQL and Python • Data modeling • Data analysis • Technical leadership • Project leadership • Completed higher education • Advanced / fluent English • Experience with Azure Data Factory, Databricks, and Azure Storage • Handling Parquet files • Spark • Senior-level Python • SQL • SQL Server and Oracle databases • Knowledge of DevOps: pipelines, branch creation, releases