Senior Manager, Applied Science
About this role
Cohere Health builds an advanced clinical intelligence platform and agentic AI-powered solutions designed to connect health plans and providers while optimizing care quality and speed. The applied science team develops, evaluates, and deploys machine learning models that automate and augment complex clinical and administrative workflows like prior authorization.
As a player-coach, the Senior Manager of Applied Science combines hands-on technical contributions with leadership responsibilities, guiding a team through the complete machine learning lifecycle from problem framing to production deployment. Candidates who thrive in this environment balance rigorous statistical methods with cross-functional collaboration, partnering closely with product, engineering, and clinical stakeholders to drive measurable healthcare impact.
What you will do
- Lead, mentor, and develop a team of Applied Scientists within a collaborative and high-performance environment.
- Translate complex healthcare business and clinical problems into scalable machine learning and data science solutions.
- Validate technical solutions using methodological and statistical rigor to guarantee reliable, reproducible, and safe clinical AI.
- Partner with AI Engineering to scale models and create tangible healthcare impact in production settings.
- Collaborate cross-functionally with Clinical Operations, Product, Engineering, and platform teams to align research with business priorities.
- Communicate technical concepts, methodologies, and insights clearly to both technical and non-technical stakeholders.
- Contribute directly to the recruiting, onboarding, and ongoing development of team members.
What you need
- Hold a PhD with at least 7 years of experience or an MS with a minimum of 10 years of experience in machine learning, data science, or applied AI roles.
- Demonstrate a minimum of 2 years of experience leading and managing technical teams.
- Bring practical experience leveraging both modern language models, including agentic and non-agentic LLMs, alongside classical machine learning techniques in production settings.
- Possess deep expertise in statistical methods, experimental design, and model evaluation.
- Show proficiency in Python alongside hands-on experience with standard machine learning frameworks like PyTorch and scikit-learn.
- Have professional experience working within AWS cloud environments.
- Provide a demonstrated track record of driving decision-making through close collaboration with business functions and product teams.
Nice to have
- Experience in transactional healthcare such as Utilization Management, Payment Integrity, Risk Adjustment, Quality, and Care Management
- Experience driving process automation in a healthcare setting using modern language models
- Experience applying retrieval and relational extraction techniques to unstructured clinical notes and other patient history documentation
Benefits
- Medical, dental, vision, life, disability insurance, and Employee Assistance Program
- 401K retirement plan with company match, flexible spending, and health savings account
- Flex Time Off plus company holidays
- Up to 14 weeks of paid parental leave
- Pet insurance
Skills and tech stack
Questions candidates ask
Is this position remote?
This is a fully remote role that may require travel to Boston, Massachusetts for new hire onboarding, occasional in-person team meetings, and company events. Overall, travel is estimated at about 15%.
What is the salary range for this role?
The annual salary range for this position is $225,000 to $240,000. Individual pay decisions are based on factors including qualifications, experience level, skillset, and internal alignment.
What does the interview process look like?
The hiring process starts with a preliminary phone screening with Talent Acquisition, followed by a meeting with the hiring manager. Candidates then complete behavioral interviews and a case study.
What tools and technologies will I use?
The tech stack involves working with Python, PyTorch, scikit-learn, and AWS. The work also incorporates modern language models in production settings alongside classical machine learning techniques.