> Markdown version of [/jobs/ext/4628538-data-scientist](https://www.wearedevelopers.com/jobs/ext/4628538-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Data Scientist

- **Company:** PAVE FINANCIAL, INC.
- **Location:** United States
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Machine Learning, Feature Engineering, Scikit Learn, Xgboost, Software Library
- **Published:** November 7, 2026
- **Apply:** https://www.careerbuilder.com/job-details/data-scientist--6ab52e87-84de-466f-b5d3-4428ad8613e7

## About the Role

Data Science, Credit Risk, Risk, Credit Reports, Cash Flow Projection, Market Segmentation, Customer Support/Service, Financial Systems, Artificial Intelligence (AI), Standards Development, in the US. Work experience in US fintech or in the financial industry. How To Apply Does this position sound like a good fit? Email us at howdy@pave.dev. Include this role's title in your subject line (it'll help us to sort through the emails). Send along links that best showcase the relevant things you've built and done.

## Description

customers' bottom lines. Your analytical skills, coupled with experience in building highly-performing statistical models and knowledge of credit risk in the US, will be instrumental in improving our data products and in influencing our product roadmap. Responsibilities Develop and maintain models (whether ML-based or heuristics-based) to enhance our product offerings. Analyze the impact of Pave's scores and attributes on customer performance metrics, particularly focusing on increasing approvals and reducing defaults. Design, implement, and evaluate experiments to test the effectiveness of new attributes and models. Collaborate with data engineers to put your models in production, monitor their performance and conduct regular updates to maintain their accuracy. Communicate findings and recommendations to stakeholders through clear, concise reports, dashboards and presentations. Stay up-to-date with industry best practices and advancements in data science, AI and ML, and credit risk
analytics tools, and technologies. Help ensure all models, attributes, and analyses are compliant with fair lending practices, translating these considerations into the attributes and analyses we conduct for credit risk. Requirements Strong analytical and data exploratory skills with the ability to ask the right questions, interpret data and provide actionable insights. Demonstrated experience of going from data analysis to building highly performing ML models, including feature engineering and measuring performance. Solid understanding of machine learning libraries like scikit-learn, and tree ensemble packages like XGBoost. Strong problem-solving skills and a detail-oriented mindset. Ability to work independently and collaboratively in a team environment. Ability to clearly communicate findings and insights to both technical and non-technical stakeholders, including external partners and customers. Knowledge and experience building credit risk models, with an understanding of credit risk