Sr. Data Scientist - AI Research

About the role

Lyra seeks a data scientist to advance its AI-powered mental health platform through rigorous analysis and model development. You will work with cross-functional teams to turn diverse data into insights that enhance patient and provider experiences. The role offers remote flexibility within the United States or a local option in Burlingame, CA.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Data pipelines are built with data engineers to apply clinical logic to raw and varied inputs, enabling structured outputs for analysis.

Actionable insights are extracted and predictive or generative models are developed using rigorous evaluation frameworks to inform product decisions.

The platform is enhanced through close collaboration with Data, Product, Engineering, and Clinical teams, aligning analytics with user needs.

Requirements

Five or more years of data analysis experience in industry settings, preferably within AI/ML product environments.

Cross-functional collaboration across technical, business, and clinical domains is required.

Statistical methods such as experimentation, probabilities, and regression are applied, with hypothesis testing used to validate findings.

Messy, irregular data from disparate sources is cleaned and processed to derive insights, regardless of necessary effort.

Complex SQL queries are written across multiple schemas and tables, with view creation preferred when applicable.

A graduate degree with MS in statistics, econometrics, biostatistics, or quantitative social sciences is expected.

Nice to have

Public health interest is considered a plus for this role.

Experience with AI/ML research, including prompt engineering and RAG, is valued.

Python is used to clean and analyze data, parse JSON structures, call APIs, create user-defined functions, and automate workflows.

Practical notes

This position can be filled remotely anywhere in the United States or locally in Burlingame, CA, with remote-based candidates physically located in the United States.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Mental health care platforms depend on data to measure access, outcomes, and cost efficiency.

AI research in this field combines statistical experimentation with product and engineering workflows.

Common tools include SQL, Python, and evaluation frameworks for model performance.

Roles in this domain often require balancing clinical logic with data quality and stakeholder communication.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.