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About This Role
#### Data Scientist, Childhood Cancer Data Lab
#### CCDL Overview
Alex’s Lemonade Stand Foundation (ALSF) is one of the leading funders of pediatric cancer research in the US and Canada. Since its inception in 2005, ALSF has funded more than 1,500 projects at nearly 150 institutions across the United States and Canada.
The Childhood Cancer Data Lab, an initiative of Alex’s Lemonade Stand Foundation, was founded in August 2017 with the mission of empowering pediatric cancer experts poised for the next big discovery with the knowledge, data, and tools to reach it. The Data Lab is comprised of a team of software developers, data scientists and designers who are driven to build software systems, analytical workflows, and training programs in service of this mission. Members of the Data Lab simultaneously contribute to childhood cancer research and to the open science and open source software communities. We build in the open and collaboratively: examples of our work include the Single\-cell Pediatric Cancer Atlas (ScPCA) Portal, OpenScPCA, and the Open Pediatric Brain Tumor Atlas (OpenPBTA). As the team grows, we are expanding our scientific capacity to support an international community of pediatric cancer researchers.
Position Overview, Duties and Responsibilities
The Data Scientist is a point person for data\-intensive cancer biology within the Data Lab. The Data Lab science team activities include a mix of research (including collaborations), short\-format training workshops, and processing and curation for data products utilized by the pediatric cancer community.
The Data Scientist is expected to identify opportunities to enhance Data Lab offerings and take a leadership role in execution. This team member will envision solutions that serve a community of dedicated scientists and clinicians, including those who receive grants from ALSF.
The Data Lab position will provide the Data Scientist with the opportunity to envision and enhance data\-processing systems as well as systems that enable the user\-guided analysis and interpretation of large\-scale, multi\-omic datasets. This member of the team will need an understanding of how to perform robust analyses of high\-dimensional biological data and to integrate across data types. The role offers latitude that grows with experience and familiarity: greater independence on work that builds on a candidate’s established expertise, and closer collaboration with the Data Science Manager and the rest of the team on newer or product\-facing efforts. Working in the Data Lab also provides a unique opportunity to interact with the childhood cancer research community and its supporters at ALSF.
- Work with the Data Lab Data Science Manager to guide the biological challenges addressed by the Lab and/or to enhance Data Labled training efforts
- Perform and summarize analyses that seek to reveal new paths to treatment of childhood cancers or enhance Data Lab offerings, in collaboration with internal teams and external partners
- Integrate and harmonize across multiple data modalities (e.g., transcriptomic, genomic, epigenomic, proteomic, and imaging data) to address questions in pediatric cancer biology
- Work with the Engineering and Design teams to design systems that address pressing need in cancer biology
- Write and review clean, maintainable source code, documentation, and instructional material for public consumption
- Represent ALSF and the Data Lab at national conferences
All employees of the Foundation undertake other duties as needed and special projects as assigned. All positions at ALSF require occasional non\-traditional work hours including evenings and weekends.
Work Location: Remote position; candidates must be available to work core hours aligned with Eastern Standard Time (EST). Candidates located within the greater Philadelphia metropolitan area are preferred.
#### Required Qualifications:
- PhD in Genetics, Genomics, Computer Science, Bioinformatics, or related field
- Expertise in the rigorous analysis of highdimensional biological data (biological data science)
- Expertise in or the ability to transition to R or Python
- Excellent written communication skills
- Must be authorized to work in the U.S., no visa sponsorship available for this role
#### Preferred Qualifications:
- Demonstrable contributions to codebases for scientific projects
- A proven record of collaborative scientific research as evidenced by peerreviewed publications or preprints
- An existing track record of cancer research
- Experience with workflow management systems (e.g., Nextflow)
- Cloud computing experience (e.g., Amazon Web Services)
- Experience developing or adapting instruction material
- Experience writing and reviewing analytical code in a collaborative environment
- Breadth across multiple data modalities is valued; experience working with singlecell or bulk transcriptomics, and/or integrating additional omics data types, is a plus
- Interest or experience in developing or applying machine learning and statistical methods for biological data
If interested, please submit your resume and cover letter describing your interest and why you are the right fit for the position. In the interest of expediting our process, only candidates who submit cover letters with resumes will be considered. Applications that include either a portfolio with work samples or demonstrable contributions to open source projects are preferred.
#### About Alexs Lemonade Stand Foundation
Founder, Alex Scott, taught us the important life lesson of “turning lemons into lemonade.” Her example of making something positive from something negative, having hope for the future, and enjoying each day is the spirit of Alex’s Lemonade Stand Foundation. The daily actions of our staff members, volunteers and researchers play an integral role in achieving the mission of the Foundation – curing all childhood cancers.
Salary Context
This $90K-$121K range is in the lower quartile for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Alex's Lemonade Stand Foundation, 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 $192,890 based on 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($105K) sits 45% below the category median. Disclosed range: $90K to $121K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Alex's Lemonade Stand Foundation AI Hiring
Alex's Lemonade Stand Foundation has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $121K - $121K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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