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Job Description

We are currently hiring a hands-on Gen AI, AI and ML Ops Engineering Lead within Data Digital and Technology (DD&T) function which is a part of Oncology Business Unit at Takeda and the Data Management Team. This is an individual contributor role, so you should be prepared to roll up your sleeves and flex a wide variety of skills. You will be able to adapt quickly to shifting priorities, manage through ambiguity, while keeping focus on key implementation opportunities.

Job responsibilities

• Act as a trusted advisor and an internal Gen AI consultant to functional stakeholders and internal Takeda Business Units and Business Functions for data science activities.

• Serve as a Lead for Gen AI use cases and operationalization on complex projects with diverse scope, including primary contact for internal liaison with Oncology US commercial and Global Commercial functions and other parts of the GDD&T organization.

• Design and build RAG service platform orchestrations including prompt engineering, guardrails, vector databases, API Grounding.

• Design, develop, and implement MLOps pipelines for Generative AI models, covering data ingestion, preprocessing, training, deployment, and monitoring.

• Automate ML tasks using GitOps, CI/CD pipelines, and containerization tools such as Docker and Kubernetes.

• Design and drive the development of models, analytical tools, and productionizing them within models hosted in Azure Open AI, AWS Sagemaker, or equivalent large language models on a modern containerized deployment stack.

• Creates database-to-deployment pipelines for models using the necessary programming languages (primarily R, Python, SQL) and tools (AWS SageMaker, Dataiku, Databricks etc.). This is a hands-on role, and you are expected to code 50%-70% of your time.

• Performs business readiness check, data access and data quality check, and hands-on exploratory data analysis to gauge the need for or appropriateness of analytical modeling.

• Acts as central steward of data quality, monitors risks through the holistic review of clinical and operational data, using detailed knowledge of the protocol, considering the specific therapeutic area aspects of the protocol related to the data collected and aligning with cross functional operational plans to drive comprehensive data science modeling tasks.

• Partner with centralized Demand Management team and GDD&T to monitor and communicate project progress to the sponsor and project team including use of project status reports and tracking tools/metrics.

• Manage external ML engineers and vendors responsible for building and maintaining ML Pipeline solutions.

• Performs other work-related duties as assigned.

Basic Requirements/Qualifications

• Bachelor's degree in Technical Disciplines (Computer Science, Data Science, Information Technology, Economics, Physics, Mathematics or equivalent).

• 8+ years of experience in data, business insights and ML experience in Life Sciences, primarily in commercial (sales, marketing, medical and access) functions.

• Deep understanding of modern data technologies, data architecture principles, and AI/machine learning, GenAI and similar technologies.

• Knowledge of medical terminology, clinical data, and commercial pharma data sources for clinical studies, in particular requirements applicable to Clinical Data Sciences.

• Experience with Generative AI, including transformers or LLM project work with frameworks, including Huggingface, Lan