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Overview:
The Accelerator seeks a Data Scientist to bolster our data team and provide insight in the data we collect.
The Accelerator at Princeton includes a portfolio of multiple planned independent and intersecting tools. Reporting to the Head of Data Science and Data Engineering, the data scientist will work within our team to help drive data science and data engineering initiatives and collaborations. They will play a crucial role in using data\-driven approaches to drive innovation, solve complex engineering problems, and ultimately advance scientific research. They will work on problems that have no precedent and little source material, requiring novel solutions. They will also be responsible for working with the other teams within the Accelerator and our external partners to help foster collaboration and create an incredibly impactful environment for our users.
This is a 6\-month term role with potential for extension.
Responsibilities:
Strategy
- Work closely with the Accelerator leadership team to align data science initiatives with overarching goals and long\-term vision.
- Identify and prioritize development projects that benefit from data science methodologies and innovations.
Data Analysis and Modeling
- Apply advanced statistical analysis, data mining, and machine learning techniques to analyze complex engineering datasets.
- Design, develop, and optimize predictive models, simulations, and algorithms to solve key engineering and scientific challenges.
- Drive the development and deployment of data\-driven products and solutions, ensuring alignment with strategic goals.
- Ensure the accuracy, integrity, and quality of data to be made available through the Accelerator.
- Collaborate with data engineering teams to optimize data workflows, enhance data accessibility, and improve performance of modeling systems.
Data Engineering
- Understand the end\-to\-end data lifecycle and its effects on machine learning models, actively enhancing and optimizing data pipelines for improved model outcomes.
- Ensure the accuracy, integrity, and quality of data to be made available through the Accelerator.
- Develop data pipelines, automation systems, and scalable infrastructure for real\-time and batch processing.
Research and Collaboration
- Work effectively in a modern, professional software and data engineering environment with a strong understanding of Agile concepts and practices.
- Modern Software Engineering Foundations: agile (Scrum), DevOps, CI/CD, and pair programming, with working knowledge of cloud compute platforms to support collaborative, scalable, and efficient development.
- Stay current with the latest advancements in data science, machine learning, and relevant engineering fields to continuously innovate.
- Collaborate with research teams to apply state\-of\-the\-art techniques to ongoing scientific challenges.
- Build strong relationships with external partners, driving collaborations that enhance the Accelerator’s scientific impact.
Qualifications:
ESSENTIAL
- 3\+ years of relevant work experience as a frontline data scientist, with a record of building innovative solutions.
- Experience working in a remote, agile environment.
- Bachelor's degree or equivalent in a relevant field
- Strong communication and interpersonal skills to effectively collaborate with researchers in the field, other engineers at various levels of experience, and administrative and leadership team members.
PREFERRED* Background in engineering principles and familiarity with various engineering disciplines.
- Publications in reputable scientific journals or conferences is desirable.
Princeton University is an Equal Opportunity Employer and all qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability status, protected veteran status, or any other characteristic protected by law.
The University considers factors such as (but not limited to) scope and responsibilities of the position, candidate's qualifications, work experience, education/training, key skills, market, collective bargaining agreements as applicable, and organizational considerations when extending an offer. The posted salary range represents the University's good faith and reasonable estimate for a full\-time position; salaries for part\-time positions are pro\-rated accordingly.
If the salary range on the posted position shows an hourly rate, this is the baseline; the actual hourly rate may be higher, depending on the position and factors listed above.
The University also offers a comprehensive benefit program to eligible employees. Please see this link for more information.
Standard Weekly Hours: 36\.25 Eligible for Overtime: No Benefits Eligible: Yes Probationary Period: 180 days Essential Services Personnel (see policy for detail): No Estimated Appointment End Date: 12/31/2026 Physical Capacity Exam Required: No Valid Driver’s License Required: Yes Experience Level: Mid\-Senior Level : \#Ll\-DP1 Salary Range: $125,000 to $138,000
Salary Context
This $125K-$138K range is below the median 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 Princeton 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 in Demand for This Role
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. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($131K) sits 34% below the category median. Disclosed range: $125K to $138K.
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.
Princeton University AI Hiring
Princeton University has 1 open AI role right now. They're hiring across Data Scientist. Based in Princeton, NJ, US. Compensation range: $138K - $138K.
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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