Jobs

Description

Remote 

Manager's Update:

• Minimum 3+ years of medical device industry exp. is required
• SAP is not mandatory - it's just a nice to have
• This is not a data engineer role. This is a Data Scientist role
• Must have exp. with Machine Learning
• Must have prior statistics analysis exp.
• Need to work with medical device data
• 8+ years of exp.
• Must have exp. with Databricks / Azure Databricks
• Prior medical device industry exp. is required
• Must have a prior medical device industry exp.
• Minimum educational qualification: Looking for Master's Degree
• Bachelor's with right years of exp. would also be considerable

• Vector AI Embeddings
• Advance statistics
• Azure Databricks
• Machine Learning

• 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.

Must Have

• Anthropic Claude AI
• Applied Machine Learning
• Applied Statistics
• Databricks Mosaic AI
• Databricks SQL
• Vector Embeddings

Nice To Have

• Power BI

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