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About This Role
Data Scientist
Organization: Humin, Inc.
Type: Full-time (exempt)
Location: Remote (U.S.-based only)
Reports to: CPO or CSO
Overview
Humin (formerly Healthy Minds Innovations) is a global nonprofit on a mission to make wellbeing real by translating science into action. Our work spans from the personal to the global. The Healthy Minds Program app-recognized by The New York Times, Healthline, Sports Illustrated, Vogue, and Business Insider-delivers free, science-based wellbeing training to millions worldwide. The Kindness Curriculum-which guided Sesame Street's season on kindness and has an Emmy–winning video series-equips children with lifelong tools for compassion. Beyond individual tools, Humin provides wellbeing science services to organizations, strengthening performance while advancing human flourishing at scale. Founded on the research of world-renowned neuroscientist Richard J. Davidson, and through ongoing work with the Center for Healthy Minds at the University of Wisconsin–Madison and other leading research institutions, we continue to turn breakthrough discovery into real-world impact. Humin is building a world that is kinder, wiser, and more compassionate, one where flourishing is possible for everyone, everywhere.
Opportunity and Summary
Humin is seeking a Data Scientist to help build the data and measurement foundation that will enable more rigorous, data-informed decision-making across the organization. Working closely with Product, Science, and Engineering, this role will help create the systems, instrumentation, and analytical approaches that will help Humin better understand how people engage with its products and how that engagement contributes to meaningful outcomes.
In the first year, this role will be primarily foundational: helping establish the instrumentation and data infrastructure that make future analysis possible, while beginning to generate insights about user behavior, engagement, retention, and outcomes with our digital products. Roughly half of the role focuses on product analytics, with the remainder spanning marketing, business, and scientific analytics. The Data Scientist will work closely across Product, Science, and Engineering to translate complex questions into clear data requirements and analytical approaches.
This role is well suited for someone who has a few years of hands-on analytics experience, can turn a stakeholder's question into clear data requirements and a sound analysis, works independently on well-scoped problems, and wants to grow alongside a data function that is still taking shape.
Key Responsibilities
Build Humin's Data & Measurement Foundation
- Help build and evolve Humin's data and measurement foundation, including event taxonomies, schemas, data quality checks, and analytical infrastructure.
- Translate Product and Science requirements into clear instrumentation specifications for Engineering, along with acceptance criteria and validation tests to confirm they were built correctly.
- Partner with Engineering to monitor instrumentation and data quality, identify issues such as tracking gaps, duplicate events, schema drift, and inconsistencies in data migrated across systems, and support remediation.
- Develop and maintain dashboards and reporting systems that make reliable data accessible across the organization.
Drive Product Analytics & Experimentation
- Analyze user behavior and product performance to deepen Humin's understanding of acquisition, activation, engagement, retention, churn, and wellbeing outcomes.
- Help define and validate what effective engagement means for Humin, linking usage patterns (including our North Star metric) to validated wellbeing outcomes.
- Apply sound analytical approaches, such as funnel, cohort, retention, and segmentation analysis, to identify meaningful patterns and distinguish signal from noise, building toward survival analysis and predictive modeling over time.
- Support Product and Science in designing and analyzing experiments and evaluating relationships between product usage and validated wellbeing measures.
- Help define and validate what effective engagement means for Humin, linking usage patterns (including our North Star metric) to validated wellbeing outcomes.
- Translate product data into clear insights and recommendations that inform product strategy, personalization, and user experience.
Apply Data & Analytics Across the Organization
- Conduct analyses that support organizational priorities, including marketing and acquisition performance, audience strategy, pipeline performance, and funder trends.
- Build and maintain reporting that helps leadership understand key performance indicators across the organization.
- Produce outcome metrics and data summaries for grant reports and funder deliverables, grounded in validated instruments.
- Translate complex analysis into clear, actionable insights for leadership, board, development, marketing, and other non-technical audiences.
Support Measurement, Research & Impact
- Support Humin's Science team with measurement selection, sample size planning, analysis, and interpretation of findings.
- With guidance from the Science team, apply psychometric principles to help ensure wellbeing outcomes are measured and interpreted rigorously.
- Translate statistical findings into accessible narratives for internal and external audiences.
- Contribute analyses to research publications, presentations, and technical reports.
Core Competencies
- Data & Measurement Systems Design: Contributes to thoughtful, scalable data and measurement systems, with a strong focus on data quality and usability.
- Analytical & Statistical Rigor: Applies appropriate analytical and statistical methods to complex questions and distinguishes meaningful signals from noise.
- Product & Business Insight: Connects data to real-world questions, translating analysis into insights that inform product, audience, and organizational decisions.
- Cross-Functional Translation & Partnership: Translates effectively across Product, Science, Engineering, and other teams, turning complex questions into clear requirements, analysis, and recommendations.
- Ownership & Adaptability: Works independently on well-scoped problems in an evolving environment, knows when to ask for input, and is comfortable investing in foundational work that enables future impact.
Ideal Candidate Profile
Required Qualifications
- 2 to 4 years of experience in data science, product analytics, or a related analytical role, or equivalent graduate research experience.
- Hands-on experience working with event or product data, including partnering with Engineering on instrumentation and data quality.
- Product analytics and applied statistical experience, including funnel, cohort, retention, and related approaches for understanding user behavior and outcomes.
- Fluency in SQL and proficiency in Python or R, with experience working with data pipelines and troubleshooting data-quality issues.
- Ability to turn a stakeholder's question into clear data requirements, and to communicate findings clearly to non-technical audiences.
- Deep interest and commitment in (or lived alignment with) wellbeing, mindfulness, and the science of human flourishing.
- We conduct background checks as part of our pre-employment screening process and follow all EEOC and FCRA regulations regarding the use of criminal records in the hiring process.
- Ability to work in the US without requiring sponsorship.
- Willingness to travel occasionally for staff gatherings and other organizational meetings and events
Preferred Qualifications
- Experience designing or analyzing experiments (for example, A/B tests), or exposure to causal inference or quasi-experimental methods.
- Working familiarity with measurement design and psychometric concepts, such as validated instruments and interpreting meaningful change, or a demonstrated ability to build it in partnership with a science team.
- Experience applying predictive modeling or survival analysis to product or user questions.
- Experience in behavioral health, wellbeing science, or another research-informed product environment.
- Experience with marketing analytics, mobile analytics, or business/revenue analytics.
- Experience with mobile SDK instrumentation, or with reconciling and migrating legacy data.
- Experience in a nonprofit or dual-revenue (earned and contributed) organization.
- We seek candidates with an awareness of and commitment to the principles of diversity, equity and inclusion across all spectrums.
Why This Role
- Work at the intersection of product, data, and wellbeing science, at an organization that cares about both engagement and real impact on people's wellbeing.
- Join Humin's data science and measurement work at an early stage, with real ownership of your work and room to grow as the function evolves.
- Help shape how Humin understands product engagement, outcomes, and impact as its work continues to grow.
- Contribute to mission-driven work focused on translating science into tools and experiences that support human flourishing.
- Collaborate closely with Humin's Science team, with opportunities to contribute to research publications.
Compensation and Benefits
The salary for this role is $125,000. To support consistency and equity in our compensation and hiring practices, this salary is fixed and is not negotiable. Humin also offers a comprehensive health and benefits package, generous paid time off, flexible/remote work arrangements, and professional development resources.
Interested?
To apply, please visit our online applicant portal at https://humin.applicantpro.com/jobs/ and click the "Apply Now" button. You will be asked to submit a resume and respond to a short set of application questions.
The application deadline for this role is Tuesday, October 20th. Applications received by this date will receive priority consideration; we may continue to review applications received after the deadline as needed.
We know there are many paths to developing the skills and experience needed to be successful in this role. We're looking for candidates who demonstrate many of the qualifications below, but we don't expect you to check every box. If you're excited about the role and believe your experience-however you've gained it-has prepared you to contribute, we encourage you to apply.
Equity and inclusion are integral to achieving the vision of Humin. Humin is committed to continuously increasing the cultural competence of its leadership and staff through organization-wide forums and professional development opportunities. We strongly encourage people from underrepresented groups to apply.
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 8,943 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Healthy Minds Innovations, 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 $190,000 based on 1,406 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Healthy Minds Innovations AI Hiring
Healthy Minds Innovations has 1 open AI role right now. They're hiring across Data Scientist. Based in US.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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