Why Charlie Health?

Millions of people across the country are navigating mental health conditions, substance use disorders, and eating disorders, but too often, they’re met with barriers to care. From limited local options and long wait times to treatment that lacks personalization, behavioral healthcare can leave people feeling unseen and unsupported.

Charlie Health exists to change that. Our mission is to connect the world to life-saving behavioral health treatment. We deliver personalized, virtual care rooted in connection—between clients and clinicians, care teams, loved ones, and the communities that support them. By focusing on people with complex needs, we’re expanding access to meaningful care and driving better outcomes from the comfort of home.

As a rapidly growing organization, we're reaching more communities every day and building a team that’s redefining what behavioral health treatment can look like. If you're ready to use your skills to drive lasting change and help more people access the care they deserve, we’d love to meet you.

About the Role

As a Staff Data Scientist, you’ll be seen as a tech lead thought leader across company leaders and across Product, Engineering, Design, Operations, and Clinical teams. You will help the company be rigorous and thoughtful on how to best use data and apply statistical methods. You will bring an experienced perspective among the team to help them polish their thinking and ultimately inform their roadmaps, shape their thinking, and drive outcomes. You will be a highly engaged cross-functional partner that is sought out to solve challenges and will be able to bridge the gap between technical and non-technical stakeholders, upleveling those around you.

We’re a team of passionate, forward-thinking professionals eager to take on the challenge of the mental health crisis and play a formative role in providing life-saving solutions. If you’re inspired by our mission and energized by the opportunity to increase access to mental healthcare and impact millions of lives in a profound way, apply today.

Responsibilities

Partner with a wide variety of stakeholders (Product, Eng, Ops, Finance, Strategy, Clinical, etc.) to turn ambiguous business questions into clear analytical recommendations that influence strategy and decision-making
Regularly influence leaders to accept recommendations and define the metrics that matter which will generate concrete and notable impact to the company’s mission
Build and establish models, methods, and analyses using appropriate statistical methods that identify key roadmap items to prioritize across the respective teams
Leverage AI to uplevel your skills and everyone around you
Leverage Bayesian and Frequentist statistical approaches to guide decision-making
Partner closely with Analytics and Data Engineering to define key objectives for the team, design and scale repeatable frameworks, and enable broader self-serve use of existing tools & methods while keeping a high bar when it comes to analytical rigor
Be a causal inference expert with knowledge across several methodologies (e.g. Experimentation, Diff-in-Diff, IV, Propensity Score, etc.) and serve as a key stakeholder in enabling this thinking
Form and vocalize opinions on how we should do things and gain trust among company leaders as a key partner they should feel empowered to reach out to for brainstorming and new initiatives
Serve as a tech lead for the Data Science org with strong mentorship of peers and are seen as the go-to expert on Data Science and can help shape the vision of the team

Requirements

7+ years in data science/analytics roles, at least 3 of those years in a tech lead capacity.
Demonstrated ability to navigate an ambiguous data environment, with start-up experience preferred related questions and provide approaches with appropriate statistical rigor to a wide variety of stakeholders.
Experience with modeling that led to clear and concrete adoption of recommendations and demonstrated visible impact
Detailed knowledge of causal inference methods including (but not limited to): RCT, Diff-in-Diff, IPW, Propensity Score, Instrumental Variables, Variance Reduction, etc.