• Operate at the intersection of data engineering, clinical science, and partner collaboration across two strategic domains: • Technical conversations with external partners (hospitals, research institutions, CROs/CMOs) and dive into the details of diverse clinical data structures. • Translate ambiguous source data into harmonized, AI-ready assets. • Map and align diverse clinical data to industry-standard biomedical ontologies. • Design, build, and maintain data dictionaries, schemas, and metadata models. • Establish, automate, and enforce data quality control (QC) and validation frameworks for incoming partner data. • Write production-grade Python code to automate data cleaning and harmonization tasks. • Practical understanding of how clinical data is generated in the real world. • Identify anomalies and hidden biases in incoming data.

• Senior professional with strong analytical skills and business acumen, responsible for understanding processes, identifying value-creation opportunities, and translating complex challenges into analytical solutions. • Will act as a strategic bridge between business areas and the technical team, designing and prioritizing data-driven initiatives based on their potential to generate value. • Facilitation and immersion: lead meetings to gather requirements, map and model processes with clients; • Solution formulation: translate client needs and challenges into analytical approaches, defining modeling assumptions and the most suitable method; • Analytics liaison: translate business challenges for the data science team, prioritizing the highest-impact initiatives; • Storytelling and executive presentation: lead presentations to client senior management, turning complex data and BI reports into clear, actionable insights; • Strategy and value generation: define metrics (KPIs), build structured business cases, and quantify the financial/strategic return of proposed solutions.

• Lead the development of evaluation frameworks for the autonomous system • Collaborate closely with Functional Safety and Systems Engineering teams • Monitor the reliability of evaluation metrics and performance data • Drive our approach to performance analysis using data-backed statistical methods • Build confidence in the evaluation framework through data-driven insights • Establish correlation between on-road and simulation data • Establish a self-service model for developers • Mentor and collaborate with fellow engineers

• Architect Document Intelligence Solutions: Design and implement advanced Machine Learning and Deep Learning models to parse, extract, and interpret text and complex chemical structures from unstructured, scanned PDF documents. • Develop LLM & Retrieval Systems: Build and optimize Large Language Model (LLM) applications, leveraging vector databases to enable semantic search, advanced data interpretation, and retrieval-augmented generation (RAG). • End-to-End ML Pipelines: Own the entire machine learning lifecycle, including data preprocessing (specifically for chemical data and OCR outputs), model training, evaluation, deployment, and post-deployment monitoring. • Bridge Chemistry & AI: Apply your chemistry domain knowledge to translate molecular structures, diagrams, and chemical data into machine-readable formats, embeddings, and actionable insights. • Cloud Architecture & Deployment: Deploy scalable, secure, and production-ready AI/ML pipelines within the AWS ecosystem, ensuring high availability and performance. • Cross-Functional Collaboration: Partner closely with software engineers, data engineers, and domain experts to integrate ML models into the core product architecture and align with business goals.