[Hiring] Senior Data Scientist @System Soft Technologies

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