• 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.

• Bridge the gap between unstructured, real-world data, and frontier AI models • Structure clinical datasets and write reproducible code • Establish, automate, and enforce data quality control (QC) and validation frameworks • Participate in technical conversations with external partners • Design, build, and maintain data dictionaries, schemas, and metadata models

Role Description We are seeking a visionary and highly skilled Senior Data Scientist to lead the development of a cutting-edge document intelligence and discovery platform. This unique role sits at the intersection of advanced Generative AI, Machine Learning, and Cheminformatics. Your primary mission will be to solve a highly complex unstructured data challenge: transforming scanned PDF documents containing intricate chemical structures into highly searchable, interpretable, and actionable knowledge bases. - 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. Qualifications - 5+ years of proven experience working as a Data Scientist, with a track record of delivering production-grade machine learning models. - A strong background in Chemistry, Cheminformatics, or a highly related scientific field, with a demonstrated ability to interpret and manipulate complex chemical structures and data types. - Advanced proficiency in Python and deep hands-on experience with the AWS cloud stack (e.g., SageMaker, Lambda, S3, EC2). - Practical, hands-on experience working with LLMs (fine-tuning, prompt engineering, or API integration) and vector databases (e.g., Pinecone, Milvus, Weaviate, or Qdrant). - Robust experience in model development, validation, deployment, and evaluation framework tools (e.g., PyTorch, TensorFlow, Scikit-Learn). - Prior experience with Computer Vision, Optical Character Recognition (OCR), or Document AI systems is highly desirable given the scanned PDF focus. - Strong analytical problem-solving skills, excellent communication, and the ability to thrive in a highly collaborative, cross-disciplinary project environment. Benefits - Ecosystem of Opportunity: You'll be part of a growing network where client engagements, thought leadership, research collaborations, and mentorship paths are interconnected. - Collaborative Environment: Our culture thrives on openness, continuous learning, and engineering excellence. - Flexible & Impact-Driven Work: Whether you're contributing from a client project, innovation sprint, or open-source initiative, we focus on outcomes—not hours. - Talent-Led Innovation: Our Innovation Community isn’t just a knowledge-sharing forum—it’s a launchpad for members to lead new projects, co-develop tools, and shape the direction of AI itself.

• Work with various ERP and IOT data sources to Build interactive and dynamic Power BI dashboards for data storytelling and stakeholder decision support • Design and develop machine learning models and predictive analytics solutions using data sourced from Snowflake • Perform data exploration, statistical analysis, and feature engineering to support AI initiatives • Collaborate with data engineering teams to ensure robust data pipelines • Contribute to the development of AI-driven tools and frameworks within the organization • Evaluate model performance and retrain as needed to adapt to evolving data • Participate in cross-functional projects involving manufacturing, quality, supply chain and engineering. • Update training, and documentation. • Deliver user training to operating groups as required. • Other duties as assigned