Principal Machine Learning Engineer, Personalization Systems

Job Description

The Data and Artificial Intelligence Platform (DAP) group is a key component of Visa’s Technology organization that provides the enabling technology and processes to handle Visa’s data assets and deliver valuable information, products, and services to customers. DAP delivers secure, high-quality, and easy-to-use Visa core data assets as a service, together with reliable and scalable data platform as a service, to enable Visa and our partners to rapidly and efficiently innovate data-driven business products and services to lead the digital payment industry.

The portfolio comprises of over 60 data products & platforms for various external clients and internal lines of business. We provide services on behalf of traditional bank customers to millions of cardholders and merchants globally. Our application development is at the forefront of technology; we are viewed as innovative leaders within our industry.

The Data Solutions & Product team is creating the next generation of scalable and responsible AI, ML and Data Innovations and products to solve client and consumer problems. We are a cross-functional team of data scientists, product managers, AI and data engineers, program managers focused on generating value for the payments ecosystem.

As a Principal ML Engineer, you will drive the research and architect our personalization platform. The role requires innovation in model optimization, system scalability, and the integration of cutting-edge AI technologies to securely redefine the customer’s personalization experience. You will build and deploy text and generation models that will have a global impact on their experiences.

Essential Functions:

• Identify opportunities to enhance personalization capabilities by diving deep into consumer and client needs.

• Develop natural language processing and personalization models that impact millions of users

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

• Iterate and fine-tune algorithms and models.

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

• Act as a thought leader, staying on top of the latest advancements in Generative AI and integrating innovative technologies into our systems to maintain a competitive edge.

• 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.

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:
Qualifications

Basic Qualifications

• 12 or more years of work experience with a Bachelor’s Degree or at least 10 years of work experience with an Advanced degree (e.g. Masters/MBA /JD/MD), or a minimum of 5 years of work experience with a PhD.

Preferred Qualifications

• 15 or more years of experience with a Bachelor’s Degree or 12 years of experience with an Advanced Degree (e.g. Masters, MBA, JD, or MD), PhD with 9+ years of experience

• 15+ years of hands-on experience developing advanced data science models

• Expertise in multiple programming languages (e.g., Python, R, Spark)

• Strengths in Deep Learning, Machine Learning, Recommendation systems, Generative models, and Statistical analyses

• Experience with data science tools and technologies (e.g., Tensor Flow, PyTorch, scikit-learn)

• Expert knowledge in Deep Learning techniques and LLM (Large Language Model).

• Experience working with Airflow, Git Hub, ML flow for building and maintaining ETL pipeline.

• Proficient in advanced data mining and statistical modeling techniques, including Predictive modeling (e.g., binomial, and multinomial regression, ANOVA), Classification techniques (e.g., Clustering, Principal Component Analysis, factor…