Sr. Data Scientist - ML Ops

To ensure that Visa s payment technology is truly available to everyone, everywhere requires the success of our key bank or merchant partners and internal business units. The Global Data Science group supports these partners by using our extraordinarily rich data set that spans more than 3 billion cards globally and captures more than 100 billion transactions in a single year. Our focus lies on building creative solutions that have an immediate impact on the business of our highly analytical partners. We work in complementary teams comprising members from Data Science and various groups at Visa. To support our rapidly growing group we are looking for ML Engineers focused on generating value for the payments ecosystem. We are dreaming of the next generation of AI features and products.

• Responsible for building end-to-end automated pipelines for AI/ML models including model training, model validation, model refit, model recalibration, model monitoring, model serving, etc.

• Work collaboratively with Data Scientists, Data Engineers and cross-functional partners to design and deploy AI/ML based solutions and products

• Lead end to-end deployment and maintenance of machine learning models in production, from concept, design, implementation, to rollout, ensuring the highest quality and performance

• Mentor team members, share knowledge, and contribute to the technical growth of the team. Provide guidance on machine learning best practices and methodologies.

• Conduct A/B tests and experiments to iterate and fine-tune algorithms and models.

• Stay updated on state-of-the-art algorithmic techniques and recognize promising innovations, adapting them to Visa s unique business and offerings.

• Design, implement, and maintain reliable, high-performance distributed systems.

• Support Strategic planning, business analysis and technical knowledge of ML Engineering, tools, and data architecture solutions.

• Strong problem-solving capabilities and ability to quickly propose feasible solutions and effectively communicate strategy and risk mitigation approaches to leadership.

Essential Functions

• Build AI/ML pipelines to support model development, model production, model validation, model performance monitoring, model recalibration, continuous integration, continuous delivery of AI/ML models.

• New model development and existing model re-training, performance evaluation and score optimization. Preferable to have data science knowledge and experience in designing, developing and implementing Deep Learning methodologies, and newer ML model implementations at scale.

• Build and maintain high performing ETL processes including data quality and testing aligned across technology, internal reporting and other functional teams.

• Build ETL pipelines in Spark, Python, HIVE or Scala, Airflow that process transaction and account level data and standardize data fields across various data sources

• Exposure to model management and governance practices. Ability to take decisions around model drift to monitor and refine models continuously.

• Create data dictionaries, setup/monitor data validation alerts and execute periodic jobs like performance dashboards, predictive models scoring for client s deliverables

• Define and build technical/data documentation and experience with code version control systems (for e.g., git). Ensure data accuracy, integrity and consistency