Data Scientist II, Applied ML
Develop and deploy machine learning models across the full lifecycle to manage financial risk and improve customer experience. The role requires 3+ years in data science or ML, strong Python and SQL skills, statistical expertise, production engineering experience, and cross-functional collaboration.
About the job
Responsibilities
- Drive data and AI solutions from inception to deployment to efficiently manage financial risk and improve customer experience.
- Own the full machine learning lifecycle, including problem identification, model design, training, productionization, and monitoring.
- Partner with Operations, Engineering, Product, Fraud, Compliance, and Credit teams.
- Follow through on business impact and support product or strategic decisions with stakeholders.
Requirements
- 3+ years of experience in data science or machine learning roles, or 2+ years with a PhD in a quantitative field.
- Demonstrated ability to own end-to-end model development, including productionization.
- Expertise in Python, SQL, and machine learning frameworks.
- Experience applying statistical techniques such as hypothesis testing and A/B testing.
- Strong software engineering fundamentals, including API development and integrating machine learning systems into production services.
- Strong communication and collaboration skills with technical and non-technical stakeholders.
Nice-to-haves
- Experience working with real-time models.
- Advanced degree or published research in machine learning or a related field.
- Experience in fraud, anti-money laundering, credit, or customer-facing machine learning models.
- Fintech industry experience.
Skills
Python, SQL, Machine Learning, Statistical Analysis, Hypothesis Testing, A/B Testing, API Development, Model Productionization, Real-Time Models, Fraud Detection, Anti-Money Laundering, Credit Risk, Fintech
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