Fraud / Credit Data Scientist, Risk Solutions
Indexed description
- 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.
- Leverage a broad spectrum of advanced statistical and machine learning methods and technologies to design flexible, scalable, and automated modeling solutions.
- Develop code and automated processes to combine and transform large volumes of data from disparate sources, to extract informative patterns.
- Keep abreast with emerging trends in machine learning and identify opportunities to leverage new tools to solve problems and improve processes
- 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
- Insights Driven: Clear hypothesis and objective driven analytics that help drive our business decisions and ongoing metrics
- Stakeholder Aligned: Understand the needs and audience for deliverables with a succinct and tailored message to maximize impact
- 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
- Quality Mindset: Trust in our findings is critical so data and analytic quality is understood and accounted for from the beginning
- Curiosity and Learning: Learn new technologies and collaborate and teach others how to use them as necessary.
- 1 to 3 years of hands-on experience in data science, machine learning, or artificial intelligence, preferably in fintech/ financial services industry
- Excellent analytical, creative problem-solving, and critical thinking skills, with the ability to tackle complex challenges and deliver innovative solutions.
- 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
- Adaptable and comfortable working collaboratively and independently in a self-starting manner
- Evidence of creative problem solving, critical thinking and a continual learning mindset
- 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 using cloud environments to develop advanced models, such as AWS Sagemaker
- Experience with end–to-end machine learning systems and MLOps framework
Pay Range: $120,900.00 - $136,800.00
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