Senior Data Scientist (Real Estate Lending Product Strategy)

Job Overview

Job Title: Senior Data Scientist (Real Estate Lending Product Strategy)

Company: Navy Federal Credit Union

Location: Vienna, VA

Job Type: Full time

Category: Data Science / Analytics / Product Strategy

Date Posted: May 18, 2026

Experience Level: 2-5 Years

Remote Status: On-site

Role Summary

• Leverage advanced data science and machine learning techniques to drive product strategy within real estate lending.
• Develop and implement descriptive, predictive, and prescriptive models to inform critical business decisions and optimize processes.
• Utilize member, financial, and organizational data to identify opportunities for new products, services, and revenue growth.
• Collaborate cross-functionally to translate complex analytical insights into actionable strategies for product development and enhancement.
• Contribute to the end-to-end model lifecycle, from conception and development to deployment and ongoing monitoring.

Primary Responsibilities

• Design, develop, and deploy sophisticated machine learning models (predictive, prescriptive, descriptive) to address key business challenges in real estate lending.
• Conduct in-depth exploratory data analysis (EDA) on large datasets to uncover trends, patterns, and insights related to member behavior, market dynamics, and product performance.
• Partner with product managers, business stakeholders, and engineering teams to define analytical requirements, scope projects, and deliver data-driven solutions.
• Build and maintain robust data pipelines and analytical frameworks to support ongoing model development, validation, and performance monitoring.
• Communicate complex analytical findings and model results clearly and concisely to both technical and non-technical audiences through compelling data storytelling and visualizations.
• Identify opportunities for process optimization within the real estate lending lifecycle through data analysis and model application, driving efficiency and member experience improvements.
• Stay abreast of the latest advancements in data science, machine learning, and AI, evaluating and recommending new technologies or methodologies to enhance analytical capabilities.
• Contribute to the documentation of models, methodologies, and analytical processes, ensuring reproducibility and knowledge sharing within the data science team.

Skills & Qualifications

Education:

• Bachelor's degree in a quantitative field such as Computer Science, Statistics, Mathematics, Economics, Engineering, or a related discipline.

Experience:

• 3-5 years of professional experience in data science, machine learning, statistical modeling, or a closely related analytical role.
• Experience within the financial services industry, particularly in real estate lending, is highly desirable.

Required Skills:

• Statistical Modeling & Machine Learning: Deep understanding and practical application of statistical concepts, regression analysis, classification, clustering, time-series analysis, and various machine learning algorithms (e.g., Random Forests, Gradient Boosting, Neural Networks).
• Programming Proficiency: Strong coding skills in Python and R for data manipulation, analysis, modeling, and visualization.
• Database Management: Expertise in SQL for querying, manipulating, and extracting data from relational databases.
• Data Analysis & Visualization: Ability to perform exploratory data analysis, interpret results, and present findings effectively using tools like Matplotlib, Seaborn, ggplot2, or similar libraries.
• Critical Thinking & Problem Solving: Demonstrated ability to break down complex problems, develop analytical approaches, and derive actionable insights.
• Communication Skills: Excellent written and verbal communication skills, with the ability to explain technical concepts to non-technical stakeholders and engage in effective data storytelling.

Preferred Skills:

• Big Data Technologies: Experience with distributed computing frameworks like Hadoop, Spark, or cloud-based big data platforms (e.g., AWS, Azure, GCP).
• Advanced Analytics Tools: Familiarity with statistical software such as SAS, SPSS, or Scala for data analysis and modeling.
• Cloud Platforms: Experience with cloud computing environments, particularly AWS (e.g., S3, EC2, SageMaker), for data storage, processing, and model deployment.
• Model Lifecycle Management: Understanding of MLOps principles and tools for managing the end-to-end model lifecycle, including deployment, monitoring, and retraining.
• Real Estate Lending Domain Knowledge: Familiarity with mortgage products, lending processes, credit risk, and relevant industry metrics.

Process & Systems Portfolio Requirements

Portfolio Essentials:

• Case Studies: Present 2-3 detailed case studies showcasing your experience in developing and deploying data science models for business impact. Each case study should clearly articulate the business problem, the data used, the methodologies applied, the results achieved, and the lessons learned.
• Metrics & ROI: Quantify the impact of your work, including specific metrics and, where possible, the Return on Investment (ROI) or business value generated by your models or analyses.
• Process Documentation: Demonstrate your ability to document analytical processes, model development steps, and data workflows clearly and comprehensively.
• System Integration Examples: If applicable, include examples of how your analytical solutions integrated with existing systems or influenced operational workflows.

Process Documentation:

• Workflow Design: Showcase examples of designing and documenting analytical workflows, from data ingestion and preprocessing to model training, evaluation, and deployment.
• Methodology Explanation: Clearly explain the statistical and machine learning methodologies used in your projects, justifying the choice of techniques based on the problem at hand.
• Performance Monitoring: Detail how you have established processes for monitoring model performance in production and implementing retraining or recalibration strategies.

Compensation & Benefits

Salary Range:

The estimated salary range for this Senior Data Scientist position in Vienna, VA, is approximately $110,500 to $141,600 per year. This range is based on industry benchmarks for similar roles in the Washington D.C. metropolitan area, considering the experience level (2-5 years) and the specialized nature of data science within real estate lending.

Benefits:

• Health, Dental, and Vision Insurance
• 401(k) Retirement Plan with Company Match
• Paid Time Off (PTO) and Holidays
• Life Insurance and Disability Coverage
• Employee Assistance Program (EAP)
• Wellness Programs and Initiatives
• Tuition Reimbursement and Professional Development Opportunities
• Potential for Bonuses and Performance-Based Incentives

Working Hours:

• Standard full-time work hours are typically 40 hours per week.
• While the role is on-site, there may be some flexibility depending on team needs and project deadlines. Some occasional overtime may be required to meet critical project milestones.

Team & Company Context

Company Culture

Industry: Financial Services (Credit Union)

Navy Federal Credit Union operates within the financial services sector, specifically as a credit union. This means it's member-owned and focuses on providing financial services to its members, often with a mission-driven approach. For a data scientist, this industry context implies working with sensitive financial data, adhering to strict regulatory compliance (like Bank Secrecy Act), and understanding the unique needs and behaviors of a member base. The "Real Estate Lending Product Strategy" focus means the role is deeply embedded in a core financial product area, requiring an understanding of market trends, credit risk, and member financial well-being.

Company Size: Large Enterprise (Implied by "FORTUNE 100 Best Companies to Work For® 2025" and extensive benefits/awards)

Navy Federal is a substantial organization, indicated by its numerous accolades and the comprehensive benefits offered. This size means opportunities for significant impact, access to extensive data resources, and potentially a more structured corporate environment with defined processes. For operations professionals, a large company often translates to more established workflows, opportunities for specialization, and a clear career progression path.

Founded: 1933

Founded in 1933, Navy Federal has a long history, suggesting stability, established operational processes, and a deep understanding of its member base. This history can also mean a blend of legacy systems and modern technologies, requiring adaptability from data scientists.

Team Structure:

• Data Science Team: Likely composed of data scientists, machine learning engineers, and data analysts, specializing in various domains. This role specifically sits within or supports the Real Estate Lending Product Strategy area.
• Reporting Structure: The role is described as an "Intermediate professional within field; requires moderate skill set and proficiency in discipline," suggesting it reports to a Data Science Manager or Lead.
• Cross-functional Collaboration: Expect close collaboration with Product Management, Real Estate Lending business units, Marketing, Risk Management, and IT/Engineering teams to define needs, implement solutions, and drive product strategy.

Methodology:

• Data-Driven Decision Making: The core of this role is using data to inform strategic decisions, emphasizing quantitative analysis and evidence-based recommendations.
• Model Development & Validation: Adherence to rigorous methodologies for building, testing, and validating models to ensure accuracy, reliability, and compliance.
• Agile/Iterative Approaches: While not explicitly stated, modern data science teams often employ iterative development cycles for model building and strategy formulation.

Company Website: https://www.navyfederal.org/

Career & Growth Analysis

Operations Career Level: Intermediate Professional (Senior Data Scientist)

This role is classified as an intermediate professional, specifically a Senior Data Scientist. This means the individual is expected to operate with a degree of autonomy, handle moderately complex projects, and apply a proficient skill set in data science and machine learning. They are beyond an entry-level analyst but may not yet be leading a team or setting long-term departmental strategy independently. The focus on "product strategy" elevates this role beyond pure technical execution, requiring strategic thinking and business impact orientation.

Reporting Structure:

The Senior Data Scientist likely reports to a Data Science Manager, Lead Data Scientist, or a Director within the Analytics or Product Strategy function. They will collaborate closely with Product Managers, Business Analysts, and stakeholders within the Real Estate Lending division.

Operations Impact:

The impact of this role is significant, directly influencing the strategy and performance of Navy Federal's real estate lending products. By providing data-driven insights and predictive models, the Senior Data Scientist will help:

• Optimize product offerings to better meet member needs and market demands.
• Improve risk assessment and mitigation strategies for lending.
• Enhance operational efficiency in the lending process.
• Identify new revenue streams and growth opportunities.

Growth Opportunities:

• Specialization: Deepen expertise in real estate lending analytics, credit risk modeling, or specific machine learning techniques.
• Leadership: Transition into Lead Data Scientist roles, managing analyti