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About This Role
ABOUT PROPRIETARY RESEARCH
On our proprietary research team—Market Intelligence—you’ll partner with our investment professionals and Compliance team to uncover insights about companies, industries, and the broader economy through deep fundamental research and applying data science and engineering techniques to alternative data sets. You’ll work alongside a talented team with diverse skills, backgrounds, and perspectives. Our researchers, product managers, and data scientists and engineers work together to build compliant research products that answer the questions posed by our investment professionals. We look for other bright, motivated, and collaborative people to join our team and grow with us—more than 90% of the leaders in our group were promoted from within.
ROLE SUMMARY
Our Data Scientists conduct research through data mining and statistical modeling to discover insights from big data that are used by our investment professionals to make investment decisions. You will have the opportunity to work in a highly collegial environment that emphasizes teaching and learning as a team. In this role, you will:
- Tackle the challenges of featuring and modeling large unstructured data using machine learning and statistical techniques
- Manage all aspects of the research and analysis process including methodology selection, data collection and quality, modeling and analysis, and performance monitoring
- Deliver research findings to investment teams, portfolio managers, and other internal clients
- Work within a team to help drive technical innovation through a collaborative R\&D process
WHAT EXCITES YOU
- Staying current on the evolving alternative data market and its role in discretionary investing
- Speaking with experts both inside and outside the Firm to understand data needs and offerings
- Multi\-tasking and switching gears frequently to address the needs of the business
- Analytical thinking and ability to sift through large unstructured data sets
- Working in a fast\-paced, dynamic environment
- Working as part of a cross\-functional team made up of investment, research, and compliance professionals
- Experiment\-based approach with freedom to innovate
WHAT EXCITES US
- Excellent attention to detail, organization, and project management skills
- Strong verbal and written communication skills
- Superb business intuition and a solution orientated, methodological approach to problem solving
- Ability to collaborate and build relationships across business units within the Firm
- People who “elevate the room” through their work ethic, curiosity, and solution\-orientated attitude
- Adherence to the highest ethical standards working closely with the Firm’s Compliance team
WHAT’S REQUIRED
- Demonstrated interest in machine learning, statistical models, and data mining tools
- Masters/PhD in a quantitative discipline w/ 1\+ year of professional experience
- Strong programming skills in Python (preferred), R, Spark, SQL
- Exceptional understanding of statistics and advanced modeling techniques
- Ability to communicate complex analyses and results clearly
WHAT SUCCESS LOOKS LIKE
- Integrity – You demonstrate 100% commitment to the highest ethical standards
- Ownership – You take charge of your work, uphold your commitments, and always do your best
- Commerciality – You focus on what matters, ask necessary questions, and are diligent about not wasting time
- Humility – You welcome and are receptive to feedback and learn from past mistakes
- Adaptability – You can triage business needs and context switch quickly and efficiently
- Admirability – You elevate the room through your work ethic, domain knowledge, and work product
WE TAKE CARE OF OUR PEOPLE
We invest in our people, their careers, their health, and their well\-being. When you work here, we provide:
- Fully paid health care benefits
- Generous parental and family leave policies
- Mental and physical wellness programs
- Volunteer opportunities
- Non\-profit matching gift program
- Support for employee\-led affinity groups representing women, minorities and the LGBTQ\+ community
- Tuition assistance
- A 401(k) savings program with an employer match and more
ABOUT POINT72
Point72 is a leading global alternative investment firm led by Steven A. Cohen. Building on more than 30 years of investing experience, Point72 seeks to deliver superior returns for its investors through fundamental and systematic investing strategies across asset classes and geographies. We aim to attract and retain the industry’s brightest talent by cultivating an investor\-led culture and committing to our people’s long\-term growth. For more information, visit www.Point72\.com/about.
The annual base salary range for this role is $125,000 \- $150,000, which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.
Salary Context
This $125K-$150K range is below the median 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 Point72, 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 ($137K) sits 29% below the category median. Disclosed range: $125K to $150K.
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.
Point72 AI Hiring
Point72 has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in New York, NY, US. Compensation range: $150K - $300K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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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