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About This Role
Affiliated Office Address
Baltimore, MD, United States
Requisition ID
121813
Date Created
July 13, 2026
Job Family
Data Management
Job Subfamily
Data Management
Job Function
Research
Exempt Status
Exempt
Shift Type
Full Time
Schedule
M\-F; 8:30am \- 5:00pm
Worksite
04\-MD:School of Medicine Campus
Work Modality
On\-site: 90\-100% of hours worked on\-site
We are seeking a *Sr. Data Scientist*. The Sr. Data Scientist will serve as a data science subject matter expert and lead the design, development, and execution of data science initiatives requiring advanced machine learning and production\-level code. The Sr. Data Scientist will research and implement state\-of\-the\-art modeling methodologies, pipelines and ML lifecycle architecture to support projects and downstream decision making. The Sr. Data Scientist will lead digital tool development efforts and conduct analytics to support full use of data procured by the Brady Urological Institute. The Sr. Data Scientist will foster collaboration efforts with data visualization specialists, data engineers, data scientists, analysts, researchers, program managers, coaches, and other external collaborators and partners in support of a portfolio of data projects.
In this role, the Sr. Data Scientist will serve as the lead computational scientist for the Cancer Ecology Center within the Brady Urological Institute, owning the design, development, and production engineering of the Center’s machine\-learning and simulation models. The Sr. Data Scientist will write and maintain the core modeling codebase — including next\-generation development of the ExposoGraph exposome knowledge\-graph platform and the build\-out of the Cancer Ecology Digital Twin (CEDT), a predictive simulation environment for modeling tumor\-ecosystem dynamics and individualized disease trajectories. The Sr. Data Scientist will leverage Python skills across four broad domains: classic ML (regression/classification tasks; e.g., XGBoost/LightGBM), deep learning (neural networks/ODEs; e.g., PyTorch), state\-of\-the\-art transformer/diffusion methodologies, and causal inference (double machine learning, ATE/CATE; e.g., CausalML), as well as utilize MLOps practices such as Git versioning and CICD pipelines. These skills will facilitate translating complex, multi\-modal biomedical and environmental datasets into validated, reproducible models that inform research and clinical decision\-making. The successful candidate will combine deep analytical modeling and machine\-learning expertise with strong software\-engineering discipline, the ability to architect data and modeling pipelines from the ground up, and a track record of leading technically rigorous projects from concept to production. Experience bridging research and applied environments, fluency in complex, interpretable, and causal machine\-learning methods, and the capacity to collaborate across data engineers, visualization specialists, clinicians, and research scientists are essential.
Specific Duties \& Responsibilities
- Design data modeling processes to build statistical and simulation models on complex data sets.
- Provide detail\-oriented and organized analytics and models to transform data sets into meaningful insights to inform stakeholder decision making.
- Research best practices and state\-of\-the\-art methodologies that can be applied in the assigned area.
- Lead the identification of data sets for modeling which leads to quantitative conclusions.
- Clean, assess quality and bias, explore, analyze, and visualize data; may delegate duties as necessary.
- Document, share, and train others on methods; may delegate duties as necessary.
- Function as a subject matter expert and/or project lead.
- Share in\-depth knowledge as a resource.
- Train data analysts on the use of software tools to carry out analysis. Update training on a regular basis.
- Develop models and tools related to the use of data. Provide input into other models and tools.
- Lead cross functional teams that may include developers, analysts, data scientists, researchers, policy experts, external partners, contractors, and vendors.
- Communicate with leadership, as well as technical and non\-technical stakeholders.
- Other duties as assigned.
Minimum Qualifications
- Master’s Degree.
- Seven years of related experience.
- Additional education may substitute for required experience and additional related experience may substitute for required education beyond a high school diploma/graduation equivalent, to the extent permitted by the JHU equivalency formula.
Preferred Qualifications
- PhD.
Technical Skills \& Expected Level of Proficiency
- Data Tools and Platforms \- Authority
- Data Tool and Resource Development \- Authority
- Data Visualization \- Authority
- Machine Learning \- Authority
- Project Management \- Authority
- Programming Languages \- Advanced
- Statistical Modeling \- Authority
- Version Control System \- Advanced
*The core technical skills listed are most essential; additional technical skills may be required based on specific division or department needs.*
Classified Title: Sr. Data Scientist
Role/Level/Range: ATP/04/PI
Starting Salary Range: $135,900 \- $238,400 Annually ($150,000 targeted; Commensurate w/exp.)
Employee group: Full Time
Schedule: M\-F; 8:30am \- 5:00pm
FLSA Status: Exempt
Location: Remote Optional
Department name: Urology
Personnel area: School of Medicine
*Total Rewards*
The referenced base salary range represents the low and high end of Johns Hopkins University’s salary range for this position. Not all candidates will be eligible for the upper end of the salary range. Exact salary will ultimately depend on multiple factors, which may include the successful candidate's geographic location, skills, work experience, market conditions, education/training and other qualifications. Johns Hopkins offers a total rewards package that supports our employees' health, life, career and retirement. More information can be found here: https://hr.jhu.edu/benefits\-worklife/.
*Education and Experience Equivalency*
Please refer to the job description above to see which forms of equivalency are permitted for this position. If permitted, equivalencies will follow these guidelines: JHU Equivalency Formula: 30 undergraduate degree credits (semester hours) or 18 graduate degree credits may substitute for one year of experience. Additional related experience may substitute for required education on the same basis. For jobs where equivalency is permitted, up to two years of non\-related college course work may be applied towards the total minimum education/experience required for the respective job.
*Applicants Completing Studies*
Applicants who do not meet the posted requirements but are completing their final academic semester/quarter will be considered eligible for employment and may be asked to provide additional information confirming their academic completion date.
*Background Checks*
The successful candidate(s) for this position will be subject to a pre\-employment background check. Johns Hopkins is committed to hiring individuals with a justice\-involved background, consistent with applicable policies and current practice. A prior criminal history does not automatically preclude candidates from employment at Johns Hopkins University. In accordance with applicable law, the university will review, on an individual basis, the date of a candidate's conviction, the nature of the conviction and how the conviction relates to an essential job\-related qualification or function.
*Diversity and Inclusion*
The Johns Hopkins University values diversity, equity and inclusion and advances these through our key strategic framework, the JHU Roadmap on Diversity and Inclusion.
*Equal Opportunity Employer*
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
*EEO is the Law*
https://www.eeoc.gov/sites/default/files/2023\-06/22\-088\_EEOC\_KnowYourRights6\.12ScreenRdr.pdf
*Accommodation Information*
If you are interested in applying for employment with The Johns Hopkins University and require special assistance or accommodation during any part of the pre\-employment process, please contact the Talent Acquisition Office at [email protected]. For TTY users, call via Maryland Relay or dial 711\. For more information about workplace accommodations or accessibility at Johns Hopkins University, please visit: https://accessibility.jhu.edu/.
*Vaccine Requirements*
Johns Hopkins University requires all faculty, staff, and students to receive the seasonal flu vaccine. Exceptions to the flu vaccine requirements may be provided to individuals for religious beliefs or medical reasons. Requests for an exception must be submitted to the JHU vaccination registry.
*The following additional provisions may apply, depending upon campus. Your recruiter will advise accordingly.*
The pre\-employment physical for positions in clinical areas, laboratories, working with research subjects, or involving community contact requires documentation of immune status against Rubella (German measles), Rubeola (Measles), Mumps, Varicella (chickenpox), Hepatitis B and documentation of having received the Tdap (Tetanus, diphtheria, pertussis) vaccination. This may include documentation of having two (2\) MMR vaccines; two (2\) Varicella vaccines; or antibody status to these diseases from laboratory testing. Blood tests for immunities to these diseases are ordinarily included in the pre\-employment physical exam except for those employees who provide results of blood tests or immunization documentation from their own health care providers. Any vaccinations required for these diseases will be given at no cost in our Occupational Health office.
Salary Context
This $135K-$238K range is above the 75th percentile for Data Scientist roles in our dataset (median: $155K across 226 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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Johns Hopkins University, 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 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $135K to $238K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Johns Hopkins University AI Hiring
Johns Hopkins University has 1 open AI role right now. They're hiring across Data Scientist. Based in Baltimore, MD, US. Compensation range: $238K - $238K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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