Director, Applied AI Engineering - Life Sciences Markets.

Accountabilities:

• Hands-On AI Development: Personally design, build, and deliver core AI services, applications, and workflows, taking ownership of critical implementation tasks and resolving technical challenges alongside engineers, data scientists, and domain specialists.

• Technical Leadership and Team Management: Lead the engineering team and oversee overall squad execution. Establish technical and implementation standards through practical contributions, review code and architecture, and coordinate priorities, dependencies, and delivery.

• Strategy and Roadmap Development: Identify high-value life sciences market questions that can be addressed using available data. Prioritize opportunities, define success metrics, and translate business needs into a focused technical and product roadmap.

• AI Architecture and Implementation: Design and implement solutions that combine machine learning models, large language models (LLMs), retrieval systems, and workflow automation. Make informed trade-offs involving model quality, reliability, latency, scalability, and cost.

• Engineering Quality and Model Evaluation: Establish automated testing, evaluation datasets, and monitoring processes to ensure the quality and reliability of AI outputs and applications. Maintain rigorous predictive model validation through point-in-time backtesting, baseline comparisons, and checks for data leakage and coverage bias.

• Production Deployment and Ownership: Move prototypes into production and take responsibility for models, APIs, integrations, deployment, observability, and incident resolution. Develop reusable technical capabilities that can scale across therapeutic areas, companies, and clients.

• Client and Domain Expert Collaboration: Work directly with clients and subject matter experts to understand analytical workflows, facilitate working sessions, explain findings, challenge assumptions, and incorporate feedback into product priorities.

• Business and Technical Alignment: Translate complex analytical findings into clear, evidence-based conclusions for senior stakeholders and clients, including communicating limitations where the available data does not support a particular conclusion.

Requirements:

• Production AI Engineering: Demonstrated experience personally building and shipping production AI applications, with end-to-end technical ownership of complex systems and measurable business outcomes.

• Technical Leadership: Proven ability to lead cross-functional technical delivery while remaining actively involved in system design, modeling, coding, debugging, and deployment.

• AI and LLM Expertise: Deep practical experience integrating machine learning models and LLMs into production systems, including retrieval, tool use, structured outputs, and evaluation methodologies.

• Model Assessment: Ability to understand model limitations, evaluate performance, and select appropriate approaches based on the available data and business requirements.

• Client-Facing Communication: Experience working directly with technical and non-technical client stakeholders, leading collaborative working sessions, and translating complex analysis into clear recommendations and conclusions.

• Data and Software Engineering: Strong hands-on experience with scalable data pipelines, automated testing, continuous integration and continuous delivery (CI/CD), and cloud infrastructure. Experience with AWS is preferred.

• AI Deployment and Infrastructure: Ability to manage both application development and AI deployment, ensuring solutions are reliable, scalable, and suitable for production environments.

• Product Development: Experience transforming bespoke analytical projects, prototypes, or pilot initiatives into repeatable products adopted by multiple clients or teams.

• Industry Knowledge: Background in life sciences, pharmaceutical analytics, or capital markets is highly advantageous but not mandatory.

• Leadership and Problem-Solving: Strong technical judgment, ownership, collaboration, and communication skills, with the ability to balance hands-on engineering responsibilities and broader delivery leadership.

Benefits:

• Remote Work: Remote position open to candidates in the United States, aligned with Eastern, Central, or Pacific time zones.

• Technical Leadership: Opportunity to lead a specialized engineering squad while remaining directly involved in designing and delivering advanced AI applications.

• Strategic Impact: Influence the development of AI-powered products that generate insights into pharmaceutical markets, treatment adoption, competitive dynamics, and commercial performance.

• Cross-Functional Collaboration: Work alongside engineers, data scientists, domain experts, and clients to solve complex analytical and technical challenges.

• Product Innovation: Build scalable AI capabilities and turn advanced technical concepts into production solutions with practical business value.