Manager of FS ML Ops and Payments Risk

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. The primary role for this position is to help set up MLOPS processes for various predictive models aimed at the growth of our payments and capital lending product while ensuring optimum risk management. You will collaborate with cross-functional teams to deliver actionable solutions that drive our organization's growth and success.

ROLE: 

• 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.

EXPERIENCE: 

• Master's or Bachelor’s degree in Data Science, Statistics, Computer Science, or Mathematics.
• Extensive experience in Payments Risk, preferable in a payments processing company
• Must have substantial experience in merchant fraud analysis specifically within the payments processing and e-commerce sectors. Familiarity with the unique challenges and fraud patterns in these industries is essential.
• In-depth knowledge of merchant fraud patterns with the ability to think creatively and analytically to connect disparate data points and proactively identify emerging fraud risks.
• Over 5+ years of industry experience building machine learning applications in large-scale distributed systems.
• Excellent problem-solving skills, analytical thinking, and thrive in a fast-paced environment.
• 5+ years of experience working within a team responsible for developing, managing, and optimizing ML models or ML infrastructure
• Strong programming skills in languages such as Python, R or Java. Expertise in SQL, and data manipulation using libraries like NumPy, Pandas, and sci-kit-learn.
• Solid understanding of machine learning techniques and algorithms, such as supervised and unsupervised learning, reinforcement learning, etc.
• Experience with ML frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Proven experience as a Data Scientist, preferably in financial services, with successful project delivery.
• Solid understanding of machine learning algorithms and statistical modelling.
• Effective communication and presentation skills for both technical and non-technical audiences.

What’s in it for you: 

• Join a growing team and help us move to the next level
• Amazing benefits & perks, including equity for all Lightspeeders
• Constant development of both your skill set and business acumen with limitless growth opportunities
• Lots of autonomy, flexible work culture and the possibility of remote work
• Innovation time to explore and learn at work
• Shaping the company by joining cultural & technical committees
• Tons of growth opportunities in technical or people management roles
• Opportunity to join a fast-paced, high-growth company  
• Opportunity to learn, expand your skill set, forge wonderful relationships and make your mark within the diverse and inclusive Lightspeed family, a true Canadian tech success story

….  And enjoy a range of benefits that will keep you happy, healthy and (not) hungry.

• Lightspeed equity scheme (we are all owners).
• Flexible paid time off and remote work policies.
• Health insurance.
• Contributions to your pension plan - RRSP.
• Health and wellness benefit of $500 per year.
• Paid leave and assistance for new parents.
• Mental health online platform and counselling & coaching services.
• Training opportunities to grow your skills and career
• Volunteer day.
• Fully stacked kitchen (hot and cold beverages, meals served) 
• Happy hours to build your relationships with colleagues after work