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About This Role
Location
Memphis, TN
Category
Computational Sciences
Department
Scientific Director's Office
Shift
Weekday Day
Position Type
Full Time
Scheduled Weekly Hours
40
Req \#
JR6953
Job Description
-------------------
The Center for Bioimage Informatics at St. Jude Children's Research Hospital is seeking a Senior Image Data Scientist to design, develop, validate, document, and operate image\-analysis pipelines and visualization solutions for biomedical imaging research. This role works closely with investigators, software engineers, data scientists, imaging facility scientists, and biostatisticians to translate scientific questions into reproducible, scalable analysis workflows using modern image analysis, statistical, computer vision, and machine learning approache
The successful candidate will support image data management, maintain analysis software and repositories, evaluate new methods and tools, and provide technical consultation and training to research teams. This role also serves as a project lead to coordinate image\-analysis efforts, align related projects, and contribute to shared CBI infrastructure that supports routine and advanced bioimage analysis.
Applicant Statement:
Applicants are encouraged to include a brief cover letter or supplemental statement with a link to representative work, such as a public repository, napari plugin, published pipeline, or comparable artifact. The statement should briefly describe one end\-to\-end image\-analysis project or software tool the applicant has owned in production, including the biological or scientific question, data scale, methods or model used, pipeline design, validation approach, user adoption, and resulting impact.
Preferred Qualifications:
- PhD in a relevant quantitative, computational, biomedical, or scientific field.
- Experience developing, validating, and maintaining image\-analysis pipelines for biological or medical imaging data.
- Strong programming skills in Python and experience with modern computer vision, machine learning, deep learning, or statistical image\-analysis methods.
- Experience with emerging AI methods relevant to bioimage analysis, including vision transformers, foundation models, generative AI, large language models (LLMs), or agentic AI tools.
- Experience with scalable and reproducible scientific workflows (e.g., Snakemake, Nextflow), containers, version control, and HPC or cloud environments.
- Demonstrated experience using modern bioimaging tools, data standards, and open\-source ecosystems such as napari, ImageJ/Fiji, Cellpose, SAM\-family and related foundation segmentation models, QuPath, BioFormats, OME\-Zarr/NGFF, or similar platforms.
- Experience with large image data volumes, image data management, quality control, benchmarking, annotation strategies, and method evaluation.
- Demonstrated ability to collaborate with research teams, communicate technical results clearly, write user\-facing documentation, and provide training through courses, workshops, seminars, or similar formats.
- Evidence of technical leadership, such as leading multi\-lab projects, contributing to shared infrastructure, publishing methods, or contributing to open\-source scientific software.
Minimum Education and/or Training:
- Bachelor's degree in applied mathematics, physics, chemistry, bioinformatics, computer science, data science, computer engineering or related field required.
- Master's degree preferred.
Minimum Experience:
- Minimum Requirement: Bachelor's degree with 3\+ years of work experience in relevant area (e.g., applied mathematics, physics, chemistry, bioinformatics, computer science, data science, computer engineering) required.
- Experience Exception: Master's degree with 1\+ years of relevant experience.
- Experience in image analyses, image data management, and programming (e.g., Python, R, Matlab, Java, C/C\+\+).
- Experience in image analysis platforms (e.g., ImageJ/Fiji, CellProfiler), scientific computing, scientific data visualization, scientific computer code optimization and evaluation in an HPC environment, development of algorithms, statistical methods or scientific software, working with large image data volumes, biological/medical imaging preferred.
- Proven performance in earlier role/comparable role.
Compensation
In recognition of certain U.S. state and municipal pay transparency laws, St. Jude is including a reasonable estimate of the compensation range for this role. This is an estimate offered in good faith and a specific salary offer takes into account factors that are considered in making compensation decisions including but not limited to skill sets, experience and training, licensure and certifications, and other business and organizational needs. It is not typical for an individual to be hired at or near the top of the salary range and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current salary range is $86,320 \- $154,960 per year for the role of Senior Image Data Scientist\-Center for Bioimage Informatics.
Explore our exceptional benefits!
St. Jude is an Equal Opportunity Employer
No Search Firms
St. Jude Children's Research Hospital does not accept unsolicited assistance from search firms for employment opportunities. Please do not call or email. All resumes submitted by search firms to any employee or other representative at St. Jude via email, the internet or in any form and/or method without a valid written search agreement in place and approved by HR will result in no fee being paid in the event the candidate is hired by St. Jude.
Salary Context
This $86K-$154K range is in the lower quartile for Data Scientist roles in our dataset (median: $157K across 236 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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At St. Jude Children's Research Hospital, 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 $198,000 based on 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($120K) sits 39% below the category median. Disclosed range: $86K to $154K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
St. Jude Children's Research Hospital AI Hiring
St. Jude Children's Research Hospital has 1 open AI role right now. They're hiring across Data Scientist. Based in Memphis, TN, US. Compensation range: $154K - $154K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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