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# Senior Data Scientist - Flex Pay

- **Company:** Upgrade Inc.
- **Location:** Charlotte, NC, United States
- **Experience:** Expert
- **Salary:** $100,000.0 - $150,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Programming, Customer Data Management, Data Systems, Data Visualization, Fraud Prevention and Detection, Python (Programming Language), Machine Learning, NumPy, Power BI, SQL Databases, Tableau (Software), Boosted Trees, Random Forest, Model Validation, Pandas, Build Management, Pyspark, Scikit Learn, Information Technology, Claude
- **Published:** October 14, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=0a37be2acfdecf48

## About the Role

* Advanced Degree (MS/PhD) in Data Science, Statistics, Mathematics, Computer Science, Finance, or a related quantitative discipline.
 * 3-5+ years of hands-on experience in a data science or analytics role, preferably in financial services.
 * Experience and/or strong interest in machine learning techniques (Random Forest, Gradient Boosted Trees, etc.) strongly preferred.
 * Strong proficiency in Python (Pandas, Numpy, Scikit-learn) and SQL.
 * Ability to write documentation and present analysis to people with different levels of expertise (e.g., technical staff, business leads, etc.).
 * Proactive, driven, and ability to work in a fast paced environment.

Nice to Have:

 * Experience with data visualization tools (e.g., Tableau, Power BI).
 * Experience with AI tools such as Claude
 * Experience with data technologies like PySpark.
 * Understanding of financial services concepts such as credit scoring, portfolio risk, or customer lifetime value.

## Description

* Build and maintain credit and fraud policy simulators used to ensure properly functioning systems and identify risk decisioning enhancements.
 * Build and deploy statistical models and machine learning algorithms to solve business problems in areas like credit risk, fraud detection, pricing, customer segmentation, and marketing attribution.
 * Validate models to identify factors that may affect model performance.
 * Analyze large, structured and unstructured datasets using SQL, Python or similar tools.
 * Stay up to date with the latest trends and technologies in data science and fintech, actively research new tools and techniques available for model development.
 * Collaborate with cross-functional teams including risk, marketing, product, and engineering to define data-driven strategies.