Data Scientist

Lendistry is an Equal Opportunity/Affirmative Action Employer. We consider applicants without regard to race, color, religion, age, national origin, ancestry, ethnicity, gender, gender identity, gender expression, sexual orientation, marital status, veteran status, disability, genetic information, or membership in any other group protected by federal, state, or local law. If you need assistance or accommodation due to a disability, you may contact us at

This position is based onsite at our Dallas, TX office. Candidates must be able to work in‑office as part of the role’s regular schedule. Some travel may be required.

• Implement, optimize, and monitor risk, fraud, line assignment, and pricing strategies within the decision engine across all lending channels, proactively identifying and resolving performance issues before they impact operations.
• Aggregate, structure, and analyze large-scale datasets using big data technologies to support credit risk modeling, fraud detection, and loss mitigation strategies.
• Develop and apply advanced statistical models and machine learning techniques to improve decision accuracy and risk management outcomes.
• Design scalable pipelines that transform raw, unstructured data into clean features for predictive modeling.
• Mine internal and external datasets to surface actionable insights into customer behavior, usage patterns, and risk indicators.
• Own analytics projects end‑to‑end from tool customization to building new analytical solutions while maintaining data integrity and pipeline reliability.
• Build and maintain tracking, monitoring, and reporting frameworks to measure the performance and impact of models, rules, and risk initiatives.
• Research and develop new methodologies and techniques to continuously improve the effectiveness of credit and fraud risk strategies.
• Partner with engineering, product, and business teams to align data science solutions with organizational goals and deliver measurable impact.

• Bachelor’s degree in a quantitative field (engineering, math, statistics, or similar) (MS/PhD preferred).
• 3+ years of hands‑on experience in business analysis, customer segmentation, and/or predictive modeling, preferably in the financial services industry.
• Proficiency in Python, SQL, and one or more additional scripting or programming languages (Java, SAS, etc.).
• Solid understanding of machine learning and statistical modeling techniques including logistic regression, gradient boosting (GBM), and clustering.
• Experience implementing credit strategies within a decision engine platform (Provenir, GDS-Link, Zoot, or similar) is a strong plus.
• Comfortable working with large datasets and translating complex analysis into clear, actionable outputs.
• Strong written and verbal communication skills; able to present findings clearly to both technical and non‑technical audiences.
• Collaborative team player who can also work independently and manage competing priorities.
• Creative, analytical thinker with a bias toward action and continuous improvement.

• Comprehensive Medical, Dental, and Vision Insurance
• Generous Paid Time Off
• Birthday Day Off
• 12 Paid Company Holidays
• 401(k) Match
• FSA and HSA
• Paid Life Insurance
• Paid Disability Insurance
• Pet Insurance
• Employee Assistance Program (EAP)
• Professional Development Courses
• In‑office provided snacks and drinks
• Gym Facilities (LA & Tustin/CEC Offices)
• In‑office engagement activities

The US base salary range for this full‑time position is $86,000‑107,600 annually. Salary is determined by role, level, and location; individual pay is based on job‑related skills, experience, and state of residence. Compensation details include only the base salary, not variable compensation.

This is a stationary position that requires frequent sitting (approximately 95%), repetitive wrist motions, grasping, speaking, listening, close vision, and the ability to adjust focus. It may also require occasional standing, lifting, carrying of 20 lbs or less, walking, kneeling, bending/stooping, twisting, pulling/pushing, and reaching above the shoulder.