RCI-BDX-3737 Senior Data Scientist (Anthropic Claude AI/Machine Learning/Databricks Mosaic AI/Databricks SQL/Vector Embeddings) (Medical Device)

Tell us about a specific analytics engineering project where you connected data from multiple source systems to create a curated, trusted, or gold-layer dataset. What systems did you work with, what business entities were involved, and what was your role in bringing the data together? Describe a time when you had to understand what the data actually represented in the business before analytics could be trusted. What was misunderstood or unclear, how did you validate the meaning, and how did that change the final dataset or metric? Tell us about one gold layer, curated data model, or analytics-ready dataset you personally helped build. What were the key fact and dimension tables, what was the grain of the data, and how did you make it usable and trustworthy for analysts or business users? Remote Assignment wil be extended or possible conversion. Applied Machine Learning , Advanced Stats Master's degree preferred Must Have • Anthropic Claude AI • Applied Machine Learning • Applied Statistics • Databricks Mosaic AI • Databricks SQL • Vector Embeddings Nice To Have • Power BI Role Overview • We are seeking a highly experienced Senior Data Scientist to drive advanced analytics across post-market surveillance, manufacturing, supplier quality, and product design. • This role will focus on identifying systemic failure patterns, enabling robust root cause analysis, and delivering proactive, AI-driven recommendations to improve product reliability and reduce operational risk. Key Responsibilities Correlate post-market data (complaints, service records, field performance) with: • Manufacturing processes • Supplier quality metrics • Product design changes • Identify emerging failure patterns and translate insights into actionable improvements • Lead end-to-end root cause investigations using structured and unstructured data • Apply AI/ML models, including LLMs, to enhance analysis, pattern detection, and signal identification • Develop and deploy advanced analytics solutions, including: • Machine learning and predictive models • Statistical analysis frameworks • Embedding-based similarity search • Design and implement agentic AI workflows to automate analysis, reasoning, and recommendations • Leverage AI models to augment decision-making and scale analytical capabilities across the organization • Partner with R&D, Quality, Regulatory, Manufacturing, and Field Service teams to translate insights into impact • Deliver proactive insights to support risk detection, product improvement, and operational excellence Required Qualifications • 7–10+ years of experience in data science, advanced analytics, or related field • Master’s degree in Data Science, Statistics, or a related discipline • Experience in medical device or regulated manufacturing environments • Strong understanding of FDA regulations and Quality Management Systems (QMS) Technical Skills • Advanced Analytics & Statistical Expertise • Strong foundation in statistical modeling and hypothesis testing • Expertise in experimental design and statistical inference (e.g., t-tests, significance testing, confidence intervals) • Ability to select and apply appropriate statistical techniques based on problem context • Experience with clustering (e.g., K-means), classification, and predictive modeling • AI/ML & Agentic AI Capabilities • Deep expertise in machine learning and advanced analytics techniques • Strong hands-on experience applying AI models (including LLMs) within analytical workflows • Experience with vector embeddings and similarity search • Ability to build and operationalize AI-driven analysis to uncover patterns and insights • Experience designing agentic AI systems for automated reasoning, investigation, and recommendations Domain & Systems Knowledge • Strong experience analyzing manufacturing and operational data • Familiarity with post-market surveillance data (complaints, service data, vigilance reporting) • Hands-on experience with SAP (Tahiti preferred), including underlying data structures • Knowledge of SAP manufacturing and quality modules • Understanding of product lifecycle data across design, manufacturing, and field performance • Behavioral & Analytical Competencies • Highly inquisitive, self-driven learner with a strong curiosity to explore complex problems • Ability to independently define analytical strategies and select appropriate methods for different scenarios • Strong command of hypothesis testing and statistical reasoning to validate findings • Deep understanding of advanced statistical techniques and experimental design • Critical thinker who can connect patterns across disparate datasets and challenge assumptions • Proactive mindset focused on continuous learning, innovation, and improvement • Ability to translate complex analysis into clear, actionable insights for business stakeholders