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About This Role
### 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 \- Growth and Marketing, you'll be seen as a thought leader among Growth, Marketing, and Revenue partners to help them be rigorous and thoughtful on how to best use data. You will bring an experienced perspective among the team to inform their roadmaps, shape their thinking, and drive outcomes. You will contribute directly to the growth of the company, via streamlining marketing measurement and approaches, leveraging statistical rigor via modelling \& causal inference to teams, and via identifying new opportunities for continued and sustained impact across the org.
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 Growth, Marketing, Revenue, and Operations to turn ambiguous business questions into clear analytical recommendations that influence strategy and decision\-making
- Build and iterate on lead, account, and ICP scoring models that help reps prioritize efforts and partner with Ops on data\-driven territory design
- Develop models to analyze successful high\-performer rep patterns to optimize onboarding and productivity
- Architect and maintain a trusted marketing measurement framework (e.g. MMM, incrementality testing \[geo tests, holdouts, lift studies]), \& align on consistent metric definitions (e.g. CAC, conversion, retention)
- Develop spend optimization \& channel allocation frameworks that account for saturation, diminishing returns, and the tradeoff between short\-term acquisition and long\-term outcomes
- Apply causal inference methods (DiD, synthetic controls, quasi\-experiments) to own channel\-level incrementality measurement, with clear decision rules \& guardrails for each approach
- Design and scale repeatable experimentation frameworks for channel, creative, and audience testing, increasing velocity without compromising rigor
### Requirements
- 7\+ years in data science/analytics roles, at least 3 of those years with Marketing, Sales, and/or Revenue teams
- 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
- Familiarity with CRMs (e.g. Salesforce), Marketing Platforms, and the metrics / terminology associated with them (CAC, Opps, etc.)
- Experience with modeling work that spans at least some of: MMM, LTV, Lead Scoring, Attribution Modeling, and Segmentation
- Strong technical proficiency: SQL, Python or R, modern ELT/ETL, OLAP databases (e.g., Snowflake), dbt, BI tools (e.g., Tableau, Hex), and experience setting up and scaling experimentation programs (A/B tests, causal inference, lift measurement)
- Excellent ability to communicate, collaborate, and influence business partners at all levels
- Bonus: Healthcare Experience, Start\-up Experience
- Passion for mission\-driven work in mental health or healthcare settings and a desire to apply your skills to improve outcomes
- Please note: candidates located within a 75\-minute commute of our NYC office are expected to work onsite 4 days/week
### Benefits
Charlie Health is pleased to offer comprehensive benefits to all full\-time, exempt employees. Read more about our benefits here.
*Additional Information*
*The total target base compensation for this role will be between $190,000 and $270,000 per year at the commencement of employment. Please note, pay will be determined on an individualized basis and will be impacted by location, experience, expertise, internal pay equity, and other relevant business considerations. Further, cash compensation is only part of the total compensation package, which, depending on the position, may include stock options and other Charlie Health\-sponsored benefits.* *\#LI\-hybrid*
### Our Values
- Connection: Care deeply \& inspire hope.
- Congruence: Stay curious \& heed the evidence.
- Commitment: Act with urgency \& don't give up.
*Please do not call our public clinical admissions line in regard to this or any other job posting.*
*Please be cautious of potential recruitment fraud. If you are interested in exploring opportunities at Charlie Health, please go directly to our Careers Page: https://www.charliehealth.com/careers/current\-openings. Charlie Health will never ask you to pay a fee or download software as part of the interview process with our company. In addition, Charlie Health will not ask for your personal banking information until you have signed an offer of employment and completed onboarding paperwork that is provided by our People Operations team. All communications with Charlie Health Talent and People Operations professionals will only be sent from @charliehealth.com email addresses. Legitimate emails will never originate from gmail.com, yahoo.com, or other commercial email services.*
*Recruiting agencies, please do not submit unsolicited referrals for this or any open role. We have a roster of agencies with whom we partner, and we will not pay any fee associated with unsolicited referrals.*
*At Charlie Health, we value being an Equal Opportunity Employer. We strive to cultivate an environment where individuals can be their authentic selves. Being an Equal Opportunity Employer means every member of our team feels as though they are supported and belong. We value diverse perspectives to help us provide essential mental health and substance use disorder treatments to all young people.*
*Charlie Health applicants are assessed solely on their qualifications for the role, without regard to disability or need for accommodation.*
*By clicking "Submit application" below, you agree to Charlie Health's* *Privacy Policy* *and* *Terms of Service.*
*By submitting your application, you agree to receive SMS messages from Charlie Health regarding your application. Message and data rates may apply. Message frequency varies. You can reply STOP to opt out at any time. For help, reply HELP.*
Salary Context
This $190K-$270K range is above the 75th percentile for Data Scientist roles in our dataset (median: $166K across 345 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 26,159 AI roles we're tracking, Data Scientist positions make up 2% of the market. At Charlie Health, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $204,700 based on 441 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($230K) sits 12% above the category median. Disclosed range: $190K to $270K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Charlie Health AI Hiring
Charlie Health has 1 open AI role right now. They're hiring across Data Scientist. Based in New York, NY, US. Compensation range: $270K - $270K.
Location Context
AI roles in New York pay a median of $200,000 across 1,670 tracked positions. That's 9% above the national median.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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