Associate Director of Machine Learning in Epidemiology and Patient Data Products

As Associate Director of Machine Learning in Epidemiology and Patient Data Products, you will lead groundbreaking research that is revolutionizing drug discovery. Our innovative platform leverages advanced computational techniques to accelerate the development of new medicines. You will lead a team of data scientists at the intersection of AI capability innovation and implementing machine learning tools to solve real patient problems that have significant impacts on global health. You will own the team’s strategic vision and collaborate with patient data, computational biology and engineering product leaders to align generative AI initiatives with patient needs. 

A successful candidate is passionate about leading high-impact teams, navigates ambiguous problems--right-sizing complex computational tasks, and delivers innovative machine learning/AI results to external stakeholders on time. 

• Lead and manage a team of three data scientists responsible for the design, implementation, and evaluation of innovative machine learning approaches in real world data (e.g., electronic medical records) for novel clinical insights.  

• Report to the Director of Epidemiology and Patient Data Products. 

• Set the technical vision for your team and partner with other teams to improve Valo’s real world data capabilities, such as care pathway optimization or patient journey mapping 

• Foster a culture of innovation, collaboration, and continuous learning to drive team performance and enhance Valo’s computational platform  

• Engage with patient data leaders and engineering product teams to align generative AI initiatives, translating complex data science concepts into compelling strategies that showcase patient-centric insights  

• Oversee multiple AI/ML projects, ensuring timely delivery, high quality, and alignment with Valo’s strategic goals. Leverage agile software tools (e.g., Jira boards) to manage timeline and resource allocation 

• Be comfortable with scientific uncertainty and embrace curiosity and creative solutions. Many of the challenges we’re trying to address don’t have known solutions or clear processes to arrive at answers 
• Work with a diverse array of global patient data spanning electronic medical records, sequencing, multi-omics data, and other data modalities using R and Python in cloud environments.  

• Master’s degree in data science, machine learning, analytics, or a related field with 10+ years of experience in machine learning or AI-focused roles, with at least 4 years in product/project leadership positions. 

• Demonstrated ability to lead high-performing teams and mentor technical talent. Minimum 2+ years of people management experience  

• Proven track record in deploying generative AI, LLMs, or agent-based solutions in production environments, preferably within healthcare   

•  Proficiency in building, fine-tuning, and deploying LLMs (e.g., GPT, BERT) and Generative AI models (e.g., transformers, diffusion models) in scalable deployment pipelines (e.g., MLOps) 

• Strong knowledge of AI/ML frameworks such as TensorFlow, PyTorch, and Hugging Face  

• Strong interpersonal and communication skills to engage effectively with engineering teams, epidemiology and biology leads, and business stakeholders 

• Familiarity with healthcare data systems (e.g., EHRs, registries, claims data) and their integration with AI models  

• Experience being an externally facing project leader, leading meetings, and soliciting input from clients, and keeping relevant stakeholders in the loop to ensure alignment throughout project lifecycle 

• Familiarity with or exposure to traditional drug discovery and development processes and approaches is a plus  

• Advanced knowledge in biostatistics approaches, including inferential and predictive modeling is a plus  

• Technical project management experience on cross-functional teams is a plus   

• Track record of innovation through patents, publications, or significant product contributions is a plus 

• Expertise in cloud platforms (e.g., AWS) and distributed computing is a plus