WEX
Fraud / Credit Data Scientist, Risk Solutions
US - Remote
Sponsorship not specified$121k-$137kDetected 6 days ago
PythonAlgorithmsSQLAWSCloud PlatformsMachine Learningscikit-learnPandasNumPyData ScienceMLOpsStatisticsResearchCommunicationCollaborationProblem SolvingCritical ThinkingAdaptability
About the role
- Who You Are You are a data scientist who excels at identifying solutions with machine learning and artificial intelligence models.
- You effectively assess how to best address a problem, recognizing where Machine Learning and Artificial Intelligence fits within a broader strategy.
- Your belief in strong communication and relationships is key to success, alongside your data and machine learning prowess.
Responsibilities
- Develop code and automated processes to combine and transform large volumes of data from disparate sources, to extract informative patterns.
- Insights Driven: Clear hypothesis and objective driven analytics that help drive our business decisions and ongoing metrics
- Results Focused: Rigorous focus on how analytics drive the end to end experiences with clear path to production and measurable impact
- Dynamic Collaboration: Drive continual improvement of our team best practices and processes to power collaboration
- Curiosity and Learnin g: Learn new technologies and collaborate and teach others how to use them as necessary.
- Excellent analytical, creative problem-solving, and critical thinking skills, with the ability to tackle complex challenges and deliver innovative solutions.
- Experience using cloud environments to develop advanced models, such as AWS Sagemaker
Requirements
- Master's or Ph.D. degree in a quantitative field such as Mathematics, Statistics, Data Science, Operations Research, Computer Science
- Advanced knowledge of SQL and experience creating and managing large datasets to organize and extract useful information
- Working knowledge of Python or R and experience with data science libraries such as lightgbm, scikit-learn, pandas, numpy etc.
- Strong communication and presentation skills with an ability to relate complex analytics findings to business outcomes
- Actual pay rates will vary and will be based on various factors, such as your qualifications, skills, competencies, and proficiency for the role.
Compensation
- $120,900.00 - $136,800.00
- The base pay range represents the anticipated low and high end of the pay range for this position.
Benefits
- include health, dental and vision insurances, retirement savings plan, paid time off, health savings account, flexible spending accounts, life insurance, disability insurance, tuition reimbursement, and more.
- Prior experience building machine learning risk models in payment processing
- Knowledge of data attributes and coverage of risk-factors for credit, fraud or other risk domains.
- Experience with end-to-end machine learning systems and MLOps framework
- Leverage a broad spectrum of advanced statistical and machine learning methods and technologies to design flexible, scalable, and automated modeling solutions.
- Keep abreast with emerging trends in machine learning and identify opportunities to leverage new tools to solve problems and improve processes
- 1 to 3 years of hands-on experience in data science, machine learning, or artificial intelligence, preferably in fintech/ financial services industry
- Evidence of creative problem solving, critical thinking and a continual learning mindset
- Data Science, Machine Learning, Statistical Learning, Artificial Intelligence, Credit Risk, Fraud, Finance, Collections, Optimization
Company info
- Learn from stakeholders and leaders on how to connect a business problem to data-driven solutions to measure and monitor risk across the firm's products and services.
- Synthesize findings into actionable insights and articulate them to the appropriate stakeholders.
- Proactively identify and communicate challenges, opportunities, and risks associated with project work to ensure timely completion of the entire product
- How you will stand out:
- For more information, check out the "About Us" section.
This listing is sourced directly from WEX's careers page and normalized into a canonical job model.