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Native Americans in Philanthropy is dedicated to increasing and nurturing Indigenous representation in the philanthropic sector. With that in mind, the opportunities on our Job Board fit one of the following criteria:    

  1. The position is within a philanthropic or nonprofit organization 
  2. The position itself is philanthropic in nature and/or focuses on roles essential to or valued by the philanthropic sector i.e. fundraising, grantmaking, gift processing or development, donor relations, nonprofit management and/or administration, social justice, equity, conservation, etc.  

Please submit your job opportunity here and note that assessment and approval of submissions can take up to 48 hours.

NOTE: Positions marked as "Featured" are either Native-focused roles or based at organizations focused on Native communities.

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Data Scientist

WellsFArgo Minnesota $100,000-$150,000
Fundraising/Development Full Time

Job Details

●​ Developed and deployed ML models for risk prediction and credit analysis, improving loan approval accuracy by 12%.

●​ Analyzed large financial datasets using Python and SQL, reducing data processing time by 30%.

●​ Applied linear and logistic regression models to analyze customer transaction behavior and predict credit risk scores, enhancing

fraud detection capabilities and reducing false positives by 18%.

●​ Built dashboard reports in Power BI to monitor portfolio performance and flag high-risk accounts, accelerating decision-making for

senior analysts.

●​ Collaborated with cross-functional teams (business analysts, engineers) to translate business requirements into data solutions.

●​ Deployed predictive models to production environments using Flask and Docker, ensuring seamless integration with Wells Fargo's

internal loan processing systems — reducing manual intervention by 20%.

●​ Built and deployed ML models for credit risk prediction using Flask ,and Docker within a CI/CD pipeline.

●​ Conducted A/B testing and model performance evaluations (using ROC-AUC, Precision, Recall) to validate and retrain models,

maintaining high model precision in fluctuating economic conditions.

●​ Delivered data-driven insights and presentations to senior leadership on portfolio performance trends, emerging fraud patterns,

and model explainability — empowering stakeholders to make faster, data-backed decisions.

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