Data Scientist I

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 hr@lendistry.com

Lendistry does not accept unsolicited resumes from recruiters, employment agencies, or staffing firms. To conduct business with Lendistry, a Master Services Agreement (MSA) must be executed and confirmed prior to submitting any information relating to a potential candidate. Without a signed MSA, Lendistry shall not be responsible to any individual or entity for any payment relating to any form of fee or compensation.

And, in the event that a resume or candidate is submitted by a recruiter, an employment agency, or a staffing firm without a fully executed MSA, Lendistry has the unrestricted right to pursue and hire any of those candidate(s) without any legal or financial responsibility to the recruiter, agency, and/or firm.

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.

A Day in the Life

The Data Scientist I will support the management and implementation of new strategies on our decision-making platform. Additionally, the Data Scientist will help shape business strategy and product development decisions through data-driven insights. This is an exciting role for someone to make a direct impact on risk, product and revenue strategy of Lendistry.

Success hinges on three things: technical aptitude to plow through data with SQL, Python, or R; quantitative ability to surface insights using statistics and ML techniques; and business acumen to measure impact through efficiency, conversion, and profit metrics. Some travel may be required.

Lendistry: Who We Are

We’re proud to be the nation’s largest minority-led, tech-savvy lender for small businesses and commercial real estate. As a certified Community Development Financial Institution (CDFI) and Community Development Entity (CDE), our mission is all about creating economic opportunities and fueling growth for small business owners and their communities. Join us as we pave the way with innovative financing and financial education!

What You’ll Be Doing

• 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.

Your Areas of Knowledge and Expertise

• 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.

Why You'll Love Working He