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Lead Machine Learning Engineer- Data - job 14 of 20

The Data and Artificial Intelligence Platform group is a key component of Visa's Technology organization, providing the enabling technology and processes to handle Visa's data assets and deliver valuable information, products, and services to customers. Our team delivers secure, high-quality, and easy-to-use Visa core data assets, along with reliable and scalable data platforms.  Our platform operationalizes the delivery of AI and decision intelligence to ensure ongoing business value to Visa and our clients.

As a Lead Machine Learning Engineer- Data with expertise in Machine Learning and Data Engineering, you will drive innovation in developing purpose-fit data solutions using cutting-edge technologies. You will be responsible for executing a data strategy that leverages our data assets to drive innovation through data engineering and GenAI solutions, including developing a synthetic data engine. You will also guide and mentor junior engineers, ensuring security, scalability, performance, and reliability in all data solutions. As an individual contributor based in Foster City, California, USA you will be reporting to Sr. Director of Data as a Service (DaaS) team. This team is growing fast! Join us and be an integral part of the group that is at the leading edge of pioneering Data and AI.

What We Offer:

  • Exciting Challenges: We work at a scale rarely seen in the industry, solving complex data and AI problems across the entire stack, offering limitless opportunities to leverage and lead in this ever-growing field.

  • Teamwork and Fun: Each of us brings diverse and rich experiences to the team, enabling us to deliver much more than the sum of our parts and have fun at the same time.

  • Fantastic People: With strong leadership, you will have the opportunity to learn, collaborate, and deliver together with some of the best and most creative minds in the industry.

About the Role:

  • Lead the design and architecture of robust, scalable data engineering platforms and infrastructure, including data pipelines, data warehouses, and data lakes (on-prem and cloud-based).

  • Oversee the entire data lifecycle, from data acquisition and ingestion to transformation, storage, and analysis.

  • Develop a synthetic data engine to generate on-demand privacy enhanced synthetic data where actual production data cannot be used, ensuring scalability and compliance.

  • Build and experiment with modeling and scoring algorithms, including custom algorithms and packaged tools based on machine learning, analytics, and statistical techniques.

  • Champion the adoption of GenAI and Agentic AI technologies and develop strategies to integrate them into existing workflows or build new ones.

  • Ensure data security, privacy, governance, and compliance with all relevant regulations, and develop and implement auditable policies and procedures.

  • Lead and guide a high-performing data engineering and ML team, fostering a culture of innovation and continuous learning.

  • Partner with business stakeholders to understand their needs and translate them into actionable data-driven solutions.

  • Stay up-to-date on emerging data and AI trends and technologies.

This is a hybrid position. Expectation of days in office will be confirmed by your hiring manager.

Average salary estimate

$150000 / YEARLY (est.)
min
max
$120000K
$180000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Lead Machine Learning Engineer- Data, Visa

If you’re passionate about Machine Learning and Data Engineering, Visa has an exciting opportunity for you as a Lead Machine Learning Engineer - Data in Foster City! Here, within the Data and Artificial Intelligence Platform group, we’re not just managing data assets; we’re turning them into powerful tools that enhance our customers’ experience. As a key player in this fast-growing team, you will be pushing the envelope by designing solutions that leverage cutting-edge technologies, including synthetic data engines and GenAI. Imagine guiding a talented group of engineers to create robust, scalable data solutions that drive innovation across the board. You'll oversee everything from data acquisition to analysis while ensuring the utmost security and compliance within our data ecosystems. The challenges you'll face are exhilarating and varied, providing opportunities to learn and grow alongside some of the brightest minds in the industry. Teamwork is our motto; we believe that a diverse pool of ideas only enhances our capability to deliver outstanding results while having fun. So if you’re ready to embark on a journey that promises excitement, challenges, and the ability to make a real impact, join us at Visa and be part of the future of Data and AI!

Frequently Asked Questions (FAQs) for Lead Machine Learning Engineer- Data Role at Visa
What are the main responsibilities of a Lead Machine Learning Engineer - Data at Visa?

As a Lead Machine Learning Engineer - Data at Visa, your primary responsibilities include designing and architecting scalable data engineering platforms, overseeing the entire data lifecycle, developing innovative data solutions, particularly a synthetic data engine for privacy preservation, leading a high-performing engineering team, and ensuring data security and compliance across all processes.

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What qualifications are needed to become a Lead Machine Learning Engineer - Data with Visa?

To be considered for the Lead Machine Learning Engineer - Data position at Visa, candidates typically need a strong background in machine learning and data engineering, along with expertise in working with data pipelines and cloud-based data platforms. Advanced degrees in computer science or a related field, along with experience in leading teams, are often preferred.

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How does Visa support the growth of its Lead Machine Learning Engineer - Data team members?

Visa is committed to fostering a culture of continuous learning and innovation for its Lead Machine Learning Engineer - Data roles. This includes mentorship opportunities, regular workshops, and collaborative projects where team members can share insights and learn from industry experts while keeping up with the latest trends in data and AI.

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What technologies do Lead Machine Learning Engineers - Data at Visa work with?

The Lead Machine Learning Engineer - Data at Visa works with a variety of cutting-edge technologies, including advanced data pipeline tools, data lakes, modeling algorithms, and GenAI technologies. This role also requires familiarity with privacy-focused data solutions and compliance regulations relevant to data handling.

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What is the work environment like for a Lead Machine Learning Engineer - Data at Visa?

The work environment for a Lead Machine Learning Engineer - Data at Visa is dynamic and collaborative. With a hybrid work arrangement in place, employees enjoy flexibility while being part of a vibrant team culture that emphasizes teamwork, creativity, and the pursuit of innovative data solutions.

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Common Interview Questions for Lead Machine Learning Engineer- Data
Can you explain your experience with building scalable data engineering platforms?

When answering this question, focus on specific projects where you’ve successfully designed and implemented data engineering solutions. Detail the technologies you used, the challenges you faced, and how your solutions achieved scalability and performance.

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What approaches do you take when developing machine learning models?

Discuss your methodology for developing machine learning models, including data preprocessing, feature selection, choosing algorithms, and model evaluation. Show your experience with both traditional methods and innovative approaches, like GenAI.

Join Rise to see the full answer
How do you ensure data privacy and compliance in your projects?

Highlight the importance of adhering to regulations. Describe processes or tools you’ve implemented to ensure data privacy, such as encryption or access controls, and your familiarity with compliance standards relevant to your role.

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What is your experience with synthetic data generation?

Elaborate on any experience you have with synthetic data engines. Discuss when to use synthetic data, challenges in its generation, and how it can drive innovation within data projects, especially regarding privacy.

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How do you mentor junior engineers in a data-driven environment?

Mention your approach to mentorship, such as hosting regular training sessions, providing constructive feedback, and collaborating with junior engineers on projects. Share examples of how you’ve helped others grow in their roles.

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Describe a challenge you faced in a data project and how you resolved it.

Use the STAR method (Situation, Task, Action, Result) to structure your response. Talk about the challenge, your role in the project, the actions you took to resolve any issues, and the impact of your solution.

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How do you stay updated with the latest trends in data and AI?

Share specific methods you use to keep up with industry trends, such as reading research papers, attending conferences, or participating in online forums. Mention any resources or communities that you find particularly valuable.

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What role does teamwork play in your data engineering projects?

Explain the importance of collaboration in your work. Discuss examples of successful team projects you’ve been a part of, emphasizing how teamwork led to better solutions and innovation in data engineering.

Join Rise to see the full answer
How would you approach a business stakeholder’s request for a data solution?

Discuss the steps you would take to understand their needs, including listening, asking clarifying questions, and translating those requirements into actionable data-driven plans. Emphasize the importance of aligning technical solutions with business objectives.

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What do you believe is the most important aspect of data governance?

When answering this question, highlight your understanding of data governance principles, including data quality, compliance, and security. Discuss how these elements contribute to building trust and integrity in data analytics.

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Visa Inc. operates as a payments technology company worldwide. The company facilitates commerce through the transfer of value and information among consumers, merchants, financial institutions, businesses, strategic partners, and government entiti...

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Full-time, hybrid
DATE POSTED
April 3, 2025

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