Senior Payments Risk Manager and ML Ops Engineer
3 weeks ago
The primary role for this position is to help set up various predictive models for payments and productionalize the MLOPs pipelines aimed at the growth of our capital lending product and payments product while ensuring optimum risk management.
Lightspeed is seeking an experienced and highly skilled Senior Payments Risk Manager with previous risk modeling and MLOps experience to join our growing team. As a Payments Risk manager and MLops engineer, you will play a crucial role in leveraging data-driven insights to enhance our business strategies, drive innovation, optimize decision-making processes and deploy credit risk metrics and models to production.
Key Responsibilities:
- MLOps Management
- Help define and own the roadmap for ML & MLOPs for LS, working collaboratively and proactively with senior architects, PMs and team leadership.
- Envision and develop new models for credit risk and fraud detection i.e work with large payment datasets to find creative new methods of detecting and deterring fraudulent behavior.
- Own production ML Ops, metrics, monitoring, alarming and logging
- Design, build and maintain experimentation framework
- Help define and implement benchmarks and metrics for model performance
- Design and implement systems that combine rules, credit risk models, feature engineering, and business and product inputs into a production pipeline.
- Design and deploy new models using tools (such as Spark, Presto, XGBoost, Tensorflow, PyTorch) and iteratively improve verification and fraud models to protect millions of users from fraud
- Data Analysis and Modeling:
- Conduct extensive data analysis using statistical and machine learning techniques to identify patterns, trends, and relationships within datasets and credit risk and payments risk.
- Develop predictive and prescriptive models to forecast market trends, fraud, delinquency, customer behaviour, and other critical business metrics.
- Utilize various data sources and data preprocessing techniques to ensure data quality and integrity.
- Business Strategy and Insights:
- Collaborate with business leaders to understand their challenges and goals, providing data-driven insights and recommendations that drive strategic decision-making.
- Identify opportunities to leverage data to improve product offerings, customer experience, and operational efficiency.
- Deliver machine learning models for use cases that have a high impact on real-world business applications and fundamentally change business processes.
- Model Validation and Performance Monitoring:
- Validate and assess the performance of developed models regularly, ensuring their accuracy, stability, and effectiveness over time.
- Implement post-processing and monitoring systems to track model performance and detect deviations, providing timely feedback to maintain model integrity.
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