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About This Role
*Navy Federal Credit Union currently does not provide sponsorship for this role. Applicants must be authorized to work in the United States without the need for current or future sponsorship.*
Navy Federal's Internal Audit team is in the midst of an exciting transformational journey to become a best\-in\-class Audit function! It is our vision to be a preferred advisor to the business by building and cultivating trust through the consistent execution of high\-quality and risk\-focused audit and advisory work. We’re focused on implementing efficient processes, maximizing our use of technology, integrating data analytics into everything we do, and investing in our biggest asset, our people. If this sounds like the type of team you’d like to be a part of, then we want to learn more about you!
Provide data\-driven insights and technology\-enabled capabilities that support Internal Audit's strategic objectives and decision\-making. Understand Internal Audit’s business needs and identify opportunities to improve audit coverage, operational efficiency, and risk insights through analytics, automation, and AI. Design, develop, and maintain audit\-focused data products, workflow automation, and AI\-enabled capabilities that support assurance and advisory activities across the audit lifecycle. Contribute to product strategy, solution delivery, testing, deployment, and continuous improvement initiatives while promoting strong data governance, data quality, and responsible AI practices.
*This position is eligible for the TalentQuest employee referral program. If an employee referred you for this job, please apply using the system\-generated link that was sent to you.*
Responsibilities
- Develop, maintain, and enhance data products, data pipelines, analytical models, dashboards, and automation solutions to support Internal Audit analytics and decision\-making
- Design, build, and enhance agentic AI and workflow automation solutions using AI technologies to drive efficiencies in audit processes
- Partner with auditors, business stakeholders, and technology teams to understand requirements and translate business challenges into scalable analytical solutions
- Support automated control testing, population\-based analytics, continuous monitoring, and audit automation initiatives
- Use modeling and trend analysis to analyze data and provide insights
- Develop understanding of best practices and ethical AI
- Transform data into charts, tables, or format that aids effective decision making
- Build working relationships with team members and subject matter experts
- Lead small projects and initiatives
- Utilize effective written and verbal communication to document and present findings of analyses to a diverse audience of stakeholders
Qualifications
- 3\-5 years of experience in exploratory data analysis
- Basic understanding of business and operating environment
- Statistics
- Programming, data modeling, simulation, and advanced mathematics
- SQL, R, Python, Hadoop, SAS, SPSS, Scala, AWS
- Model lifecycle execution
- Technical writing
- Data storytelling and technical presentation skills
- Research Skills
- Interpersonal Skills
- Working knowledge of procedures, instructions, and validation techniques
- Model Development
- Communication
- Critical Thinking
- Collaborate and Build Relationships
- Initiative with sound judgement
- Technical (Big Data Analysis, Coding, Project Management, Technical Writing, etc.)
- Sound Judgment
- Problem Solving (Responds as problems and issues are identified)
- Bachelor's Degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or degrees in similar quantitative fields
Desired Qualifications
- Master's/PhD Degree in Data Science, Statistics, Mathematics, Computer Science, or Engineering
- Experience with Alteryx, Databricks, Azure Data Lake, Microsoft Copilot Studio, Power Automate, Power Apps and Azure AI Foundry
- Experience using Python and related data science libraries such as Pandas, NumPy, PySpark, Scikit\-learn, or similar frameworks for data analysis, automation, and model development
- Experience building dashboards and reporting solutions using Power BI or similar visualization tools
- Experience managing the end\-to\-end product and solution lifecycle, including requirements gathering, solution design, testing, user acceptance testing (UAT), deployment, and ongoing support
- Knowledge of data governance, data quality controls, data lineage, and data management best practices
- Experience working within audit, risk management, compliance or financial services
- Experience working in Agile product development environments and collaborating with cross\-functional teams
- Demonstrated curiosity, innovation mindset, and ability to identify opportunities to automate manual processes
Additional Information
Hours:
- Monday \- Friday, 8:00AM \- 4:30PM
Location:
- 820 Follin Lane, Vienna, VA 22180
- 5510 Heritage Oaks Drive, Pensacola, FL 32526
- 141 Security Drive, Winchester, VA 22602
About Us
Navy Federal provides much more than a job. We provide a meaningful career experience, including a culture that is energized, engaged and committed; and fierce appreciation for our teams, who are rewarded with highly competitive pay and generous benefits and perks.
Our approach to careers is simple yet powerful: Make our mission your passion.
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- Military Times 2025 Best for Vets Employers
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- 2025 RippleMatch Campus Forward Award Winner for Overall Excellence
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- 2025 Handshake Early Talent Award
From *Fortune* . ©2025 *Fortune* Media IP Limited. All rights reserved. Used under license. *Fortune* and *Fortune* Media IP Limited are not affiliated with, and do not endorse products or services of, Navy Federal Credit Union.
Equal Employment Opportunity: All qualified applicants will receive consideration for employment without regard to age, race, sex, color, religion, national origin, disability, veteran status, pregnancy, sexual orientation, genetic information, gender identity or any other basis protected by applicable law.
Accommodations: If you need accommodation or assistance for a qualifying condition to complete the online application (or during any stage of the hiring process), you can contact Navy Federal's Medical Accommodations team at [email protected] or by calling 1\-888\-503\-6013\. This team cannot provide any information on job postings or application status.
Disclaimers: Navy Federal reserves the right to fill this role at a higher/lower grade level based on business need. An assessment may be required to compete for this position. Job postings are subject to close early or extend out longer than the anticipated closing date at the hiring team’s discretion based on qualified applicant volume. Navy Federal Credit Union assesses market data to establish salary ranges that enable us to remain competitive. You are paid within the salary range, based on your experience, location and market position. For additional details regarding compensation and benefits, review the Benefits page of the Navy Federal Career Site.
Protect Yourself from Job Scams: Navy Federal Credit Union jobs are posted on our career site, jobs.navyfederal.org and reputable job boards (e.g., LinkedIn, Indeed). We do not post jobs on social media marketplaces, messaging apps or unverified websites. We will never ask candidates for payment, bank details or personal financial information during the hiring process.
Bank Secrecy Act: Remains cognizant of and adheres to Navy Federal policies and procedures, and regulations pertaining to the Bank Secrecy Act.
Salary Context
This $99K-$155K 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 Navy Federal Credit Union, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($127K) sits 34% below the category median. Disclosed range: $99K to $155K.
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.
Navy Federal Credit Union AI Hiring
Navy Federal Credit Union has 1 open AI role right now. They're hiring across Data Scientist. Based in Vienna, VA, US. Compensation range: $155K - $155K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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