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About This Role
The Lead Data Scientist for Market Regulation Technology will work to establish and institutionalize data science and machine learning best practices as FINRA moves to ML based pattern development. The development product for this move will be the creation of the Dynamic Surveillance Platform. The lead data scientist will have primary responsibility of the development of the Machine Learning (ML) Framework portion of the Platform.Essential Job Functions:
- Lead development of the ML Framework within the Dynamic Surveillance Platform
- Work closely with multiple FINRA businesses to specify, design and build the ML Framework
- Collaborate with Data Engineers to specify and design the Data Framework
- Strong and disciplined coding skills with a history of creating and supporting reusable packages (Scala, R or Python)
- Analyze experimental data with statistical rigor. Ensure accurate interpretation by combining business acumen with detailed data knowledge and statistical expertise
- Build machine learning base surveillance models to monitor markets and protect investors
- Translate analytic insights into concrete, actionable recommendations for new models and communicate these findings
- Drive efforts to enable independent interpretation of results through education, improved tools, and data visualization
- Demonstration of FINRA’s values.
- Collaboration, both in\-person and virtually, in furtherance of FINRA’s mission of investor protection and market integrity.
Other Responsibilities:
- Mentor other Data Scientists on the team and within FINRA on methodologies and best practices
Education/Experience Requirements:
- Masters or PhD degree (preferred) in Statistics, Mathematics, Psychology, Sociology, Econometrics, or related field.
- 5\+ years relevant experience with a proven track record of leveraging analytics and large amounts of data to drive significant business impact
- Solid statistical knowledge and intuition, ideally utilized in experimentation and modeling
- Strong algorithmic thinking and independent research ability
- Passion for learning and innovating new methodologies at the intersection of statistics, applied math, and computer science
- Exceptional interpersonal and communication skills coupled with strong business acumen.
- Must be able to translate business objectives into actionable analyses, and analytic results into actionable business and product recommendations
- Impactful presentation skills, including the use of meaningful charts, graphs, or other data visualizations to convey information and results clearly and concisely
- Strong background in machine learning
- Strong knowledge of advanced statistical methods and predictive modeling
Work Conditions:
- Hybrid work environment, with defined in\-person presence requirements.
- Occasional travel and extended hours may be required.
For work that is performed in CO, FL, TX, IL, PA, MA, MD, VA, Washington, DC, NY and NJ, please refer to the chart below for the salary range for the corresponding location. FINRA complies with all state and local pay transparency laws and regulations requiring the disclosure of salary ranges for the position. In addition to location, actual compensation is based on various factors, including but not limited to, the candidate’s skill set, level of experience, education, and market considerations.
CO/FL/TX: Minimum Salary $114,200, Maximum Salary $207,200
IL/PA: Minimum Salary $125,900, Maximum Salary $228,000
MA/MD/VA/Washington, DC: Minimum Salary $131,200, Maximum Salary $238,300
NY/NJ: Minimum Salary $131,200, Maximum Salary $248,700
\#LI\-DNI
To be considered for this position, please submit an application. Applications are accepted on an ongoing basis.
*The information provided above has been designed to indicate the general nature and level of work of the position. It is not a comprehensive inventory of all duties, responsibilities and qualifications required.*
*Please note: If the “Apply Now” button on a job board posting does not take you directly to the FINRA Careers site, enter www.finra.org/careers into your browser to reach our site directly.*
Employees may be eligible for a discretionary bonus in addition to base pay. Non\-exempt employees are also eligible for overtime pay in accordance with federal, state, or local law. As part of its dedication to employee wellness, FINRA provides comprehensive health, dental and vision insurance. Additional insurance includes basic life, accidental death and dismemberment, supplemental life, spouse/domestic partner and dependent life, and spouse/domestic partner and dependent accidental death and dismemberment, short\- and long\-term disability, long\-term care, business travel accident, disability and legal. FINRA offers immediate participation and vesting in a 401(k) plan with company match and eligibility for participation in an additional FINRA\-funded retirement contribution, tuition reimbursement, commuter benefits, and other benefits that support employee wellness, such as adoption assistance, backup family care, surrogacy benefits, employee assistance, and wellness programs.
Time Off and Paid Leave\*
FINRA encourages its employees to focus on their health and wellness in many ways, including through a generous time\-off program of 15 days of paid time off, 5 personal days and 9 sick days, unless otherwise required by law (all pro\-rated in the first year). Additionally, we are proud to support our communities by providing two volunteer service days (based on full\-time schedule). Other paid leave includes military leave, jury duty leave, bereavement leave, voting and election official leave for federal, state or local primary and general elections, care of a family member leave (available after 90 days of employment); and childbirth and parental leave (available after 90 days of employment). Full\-time employees receive nine paid holidays.
- Based on full\-time schedule
Important Information
FINRA’s Code of Conduct imposes restrictions on employees’ investments and requires financial disclosures that are uniquely related to our role as a securities regulator. FINRA employees are required to disclose to FINRA all brokerage accounts that they maintain, and those in which they control trading or have a financial interest (including any trust account of which they are a trustee or beneficiary and all accounts of a spouse, domestic partner or minor child who lives with the employee) and to authorize their broker\-dealers to provide FINRA with duplicate statements for all of those accounts. All of those accounts are subject to the Code’s investment and securities account restrictions, and new employees must comply with those investment restrictions—including disposing of any security issued by a company on FINRA’s Prohibited Company List or obtaining a written waiver from their Executive Vice President—by the date they begin employment with FINRA. Employees may only maintain securities accounts that must be disclosed to FINRA at one or more securities firms that provide an electronic feed (e\-feed) of data to FINRA, and must move securities accounts from other securities firms to a firm that provides an e\-feed within three months of beginning employment.
You can read more about these restrictions here.
As standard practice, employees must also execute FINRA’s Employee Confidentiality and Invention Assignment Agreement without qualification or modification and comply with the company’s policy on nepotism.
Search Firm Representatives
Please be advised that FINRA is not seeking assistance or accepting unsolicited resumes from search firms for this employment opportunity. Regardless of past practice, a valid written agreement and task order must be in place before any resumes are submitted to FINRA. All resumes submitted by search firms to any employee at FINRA without a valid written agreement and task order in place will be deemed the sole property of FINRA and no fee will be paid in the event that person is hired by FINRA.
FINRA is an Equal Opportunity Employer
All qualified applicants receive consideration for employment without regard to any legally protected category, including race, color, age, national origin, ethnicity, religion, disability, genetic information, military or veteran status, sex, or any other status or classification protected by state or local law.
FINRA strives to make our career site accessible to all users. If you need a disability\-related accommodation for completing the application process, please contact FINRA’s Employee Relations team at 240\-386\-4865 or by email at EmployeeRelations@FINRA.org. Please note that this process is exclusively for inquiries regarding accommodations in the application process.
FINRA abides by the requirements of 41 CFR 60\-741\.5(a). This regulation prohibits discrimination against qualified individuals on the basis of disability and requires affirmative action by covered prime contractors and subcontractors to employ and advance in employment qualified individuals with disabilities.
FINRA abides by the requirements of 41 CFR 60\-300\.5(a). This regulation prohibits discrimination against qualified protected veterans and requires affirmative action by covered prime contractors and subcontractors to employ and advance in employment qualified protected veterans.
©2026 FINRA. All rights reserved. FINRA is a registered trademark of the Financial Industry Regulatory Authority, Inc.
Salary Context
This $114K-$248K range is above the median 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 FINRA, 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. This role's midpoint ($181K) sits 6% below the category median. Disclosed range: $114K to $248K.
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.
FINRA AI Hiring
FINRA has 1 open AI role right now. They're hiring across Data Scientist. Based in Rockville, MD, US. Compensation range: $248K - $248K.
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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