Sr. Machine Learning Scientist

Company Description

Visa is a world leader in payments and technology, with over 259 billion payments transactions flowing safely between consumers, merchants, financial institutions, and government entities in more than 200 countries and territories each year. Our mission is to connect the world through the most innovative, convenient, reliable, and secure payments network, enabling individuals, businesses, and economies to thrive while driven by a common purpose – to uplift everyone, everywhere by being the best way to pay and be paid.

Make an impact with a purpose-driven industry leader. Join us today and experience Life at Visa.

Job Description

In Ecosystem & Operational Risk group, Payment Fraud Disruption team is responsible for building critical risk and fraud detection and prevention applications and services at Visa. This includes idea generation, architecture, design, development, and testing of products, applications, and services that provide Visa clients with solutions to detect, prevent, and mitigate fraud for Visa and Visa client payment systems. 

The candidate for this role need to have strong ML and Data Science background, with demonstrated experience in building, training, implementing and optimized advanced AI models for payments, risk or fraud prevention products that created business value and delivered impact within the payments or payments risk domain or have experience building AI/ML solutions for similar industries.  

This role represents an exciting opportunity to make key contributions to strategic offering for Visa. This candidate needs to have strong academic track record and be able to demonstrate excellent software engineering skills. The candidate will be a self-starter comfortable with ambiguity, with strong attention to detail, and excellent collaboration skills. 

The ideal candidate will bring the excitement and passion to leverage traditional and generative AI to advance existing fraud detection mechanisms and to innovate and solve new fraud use cases. This engineer will help design, enhance, and build next generation fraud detection solutions in an agile development environment. 

Essential Functions 

• Develop new models and re-train existing ones, evaluate performance, and optimize scores. Preferable to have data science knowledge and experience in designing, developing, and implementing Deep Learning methodologies and scalable ML models.

• Devise deep learning architectures and algorithms for graph-based data, integrating Graph Neural Networks (GNNs) and advanced graph representation learning techniques.

• Implement efficient methods for monitoring model effectiveness and performance in production.

• Build ETL pipelines using Spark, Python, HIVE, Scala, or Airflow to process transaction and account-level data, and standardize data fields from various sources.

• Experiment with and develop custom algorithms for modeling and scoring, utilizing machine learning, data mining, and statistical techniques.

• Collaborate with Data Scientists, Data Engineers, Software Engineers, and cross-functional partners to design and deploy AI/ML solutions and products.

• Lead the end-to-end deployment and maintenance of machine learning models in production, ensuring high-quality performance throughout the process.

This is a hybrid position. Hybrid employees can alternate time between both remote and office. Employees in hybrid roles are expected to work from the office 2-3 set days a week (determined by leadership/site), with a general guidepost of being in the office 50% or more of the time based on business needs.

Qualifications

Basic Qualifications:
• 2 or more years of work experience with a Bachelor’s Degree or an Advanced Degree (e.g. Masters, MBA, JD, MD, or PhD)

Preferred Qualifications:
• 3 or more years of work experience with a Bachelor’s Degree or more than 2 years of work experience with an Advanced Degree (e.g. Masters, MBA, JD, MD)
• Degree in Computer Science, Data Science, Machine Learning, AI, or a related STEM field
• Experience in machine learning, Graphical Neural Networks (GNNs), knowledge graphs, and graph-based technology and analysis.
• Proficiency in SQL for data extraction and aggregation.
• Experience with Big Data and analytics technologies like Hadoop, Spark, Scala, and MapReduce.
• Proficiency in Python and deep learning frameworks and libraries such as PyTorch and PyG.
• Skilled in advanced data mining and statistical modeling techniques, including predictive modeling, classification, and decision trees.
• Experience with large-scale data ingestion, processing, and storage in big data platforms (Hadoop) and common database systems and data formats (Parquet, Avro).
• Familiarity with Linux, shell scripting, and commonly