Associate Director, AI and Data Scientist

* ** AI and ML Models:
** Experiment with, develop and train or fine-tune high quality effective AI models for business problems and processes, validate and evaluate them for fielding as part of broader solutions. Demonstrate strong foundational understanding of AI/ML, statistics, and data science concepts.
* ** Generative AI:
** Expertise in generative AI, including concepts like prompt engineering, embeddings, and fine-tuning, is often required for building and upgrading modern AI solutions. Core understanding of evaluation of LLMs quantitatively and qualitatively. Hands-on experience demonstrated in developing and fielding enterprise fieldable AI systems. Investigate and conduct Proof of Concept (PoC) initiatives and develop solutions for new AI applications using advanced technologies like Large Language Models (LLMs) and Generative AI (Gen-AI) to enhance data analytics capabilities to advance and effectively accelerate candidates across drug development phases.
* ** Understanding MLOps and LLMOps:
** Possess strong knowledge of processes and tools for deploying and maintaining machine learning models, LLM’s, and agents in a production environment. Oversee the life cycle management and revisions of AI solutions
- ** Agentic AI frameworks and architecture:
** Design**,
** implement and deploy of agentic AI systems utilizing perception, planning, reasoning, orchestration, execution, and reflection loops. Demonstrate deep previous experience in architecting and deploying AI agent based solutions.
* Expertise in real-world data assets and using them to generate scientific evidence and guide operational effectiveness and efficiencies.
* Deep expertise across data engineering, representation, Gen AI, AI and machine learning techniques and experience in architecting and delivering AI/ML use cases.
* Experience in AI product development with focus on leveraging AI, Data Science, Machine Learning. Deep understanding of AI and Machine Learning and its applications in Pharma
* Experience with data science and AI enabling technology, such as Dataiku Data Science Studio, Snowflake, AWS Sage Maker or other data science platforms and ability to maintain awareness as new AI technologies emerge
* Creative problem solving using responsible use of AI and other technologies.
* Excellent communication and stakeholder management skills, with the ability to convey complex technical concepts to non-technical audiences.
* Familiarity with machine learning and AI technologies and their integration with data engineering pipelines
* Strong understanding of Software Development Life Cycle (SDLC) and data science development lifecycle (CRISP). Awareness of testing and validation approaches related to GxP, non-GxP, etc.
* Highly self-motivated to deliver both independently and with strong team collaboration, leverage roles that exist in the team and in the larger ecosystem
* Experience in AI and ML based software/product engineering; familiarity with test and validation principles, GxP validation
* Experience with data science enabling technology, such as Dataiku Data Science Studio, Snowflake, AWS Sage Maker or other data science platforms
* Experience in architecting, building and maintaining large-scale data and AI solutions in a scientific, regulated, or research-heavy environment.
* Strong experience working within the pharmaceutical, biotech, or life sciences industry, particularly  in drug development and clinical trials is highly desirable.
* Proven track record of implementing and deploying Gen AI and large language model (LLM) applications in production environments.
* Expertise in real-world data assets and using them to generate scientific evidence and guide operational effectiveness and efficiencies.
* Deep expertise across data engineering, representation, Gen AI, AI and machine learning techniques and experience in architecting and delivering AI/ML use cases.
* Strong internal and cross-functional collaboration, project management skills with a focus on delivering impactful initiatives.
* Understanding of life sciences R&D business processes.
* Experience working with relevant life sciences datasets such as claims, clinical…