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Lead Machine Learning Engineer- Data - job 10 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.)
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$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

Are you ready to take your career to the next level? As a Lead Machine Learning Engineer- Data at Visa in Foster City, you're in for an incredible journey. You'll join the Data and Artificial Intelligence Platform group, a crucial part of Visa's Technology organization that manages our expansive data assets and delivers cutting-edge services to customers. This is not just any role; you'll be at the forefront of innovation, driving the development of purpose-fit data solutions using the latest technologies. Imagine overseeing the entire data lifecycle, from acquisition to analysis, while mentoring a talented team of junior engineers. With your expertise in data engineering and Machine Learning, you'll design scalable platforms, develop a synthetic data engine, and implement GenAI solutions that provide real business value. Visa's commitment to maintaining a collaborative and fun workplace means you'll work alongside some of the industry’s brightest minds, tackling complex data challenges at an unprecedented scale. This hybrid position allows flexibility while staying engaged with your team. If you're passionate about shaping the future of data and AI and keen on delivering impactful solutions, Visa is the perfect place for you to thrive. Join us for the adventure that awaits as we lead the charge in data innovation!

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

As a Lead Machine Learning Engineer- Data at Visa, you will spearhead the design and implementation of robust data engineering platforms, oversee the entire data lifecycle, and develop innovative solutions such as synthetic data engines. You will also guide a high-performing team, ensuring data security and compliance while collaborating closely with business stakeholders to meet their data-driven needs.

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What qualifications are needed for the Lead Machine Learning Engineer- Data position at Visa?

To qualify for the Lead Machine Learning Engineer- Data role at Visa, candidates should possess a strong background in machine learning and data engineering, with extensive experience in designing scalable data platforms. A deep understanding of data lifecycle management, along with demonstrated leadership skills—especially in mentoring junior engineers—is also essential.

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How does the Lead Machine Learning Engineer- Data role at Visa contribute to AI and innovation?

In the Lead Machine Learning Engineer- Data role at Visa, you'll play a pivotal part in driving AI adoption and innovation. Your work will involve developing GenAI and Agentic AI technologies, ensuring efficient integration into existing workflows, and cultivating a culture of continuous learning and experimentation, thus greatly influencing Visa's data strategy.

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What kind of projects will the Lead Machine Learning Engineer- Data work on at Visa?

The Lead Machine Learning Engineer- Data at Visa will tackle challenging projects like designing cloud-based data infrastructures, building scalable data pipelines, and creating algorithms for machine learning and analytics. You'll also focus on crucial initiatives like developing synthetic data solutions for privacy compliance, making your work at Visa both impactful and innovative.

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Is the Lead Machine Learning Engineer- Data job at Visa a remote position?

The Lead Machine Learning Engineer- Data position at Visa is a hybrid role, allowing for a mix of in-person and remote work. Details regarding the number of days in the office will be discussed with your hiring manager, ensuring flexibility while maintaining team collaboration.

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Common Interview Questions for Lead Machine Learning Engineer- Data
Can you describe your experience with machine learning and data engineering?

In your response, highlight specific projects and technologies you've worked with, emphasizing your ability to implement machine learning algorithms and manage data pipelines. Talk about how these experiences make you a great fit for the Lead Machine Learning Engineer- Data role at Visa.

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What is your approach to designing scalable data platforms?

Explain your methodology in designing data platforms, discussing important considerations like data throughput, latency, and storage solutions. Illustrate your answer with examples of past designs that have worked effectively in production environments.

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How do you ensure data security and compliance in your projects?

Share your understanding of data governance best practices and regulatory compliance. Provide examples of how you've implemented security measures in previous projects, emphasizing your commitment to responsible data management.

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Describe a challenging data problem you solved and how you approached it.

Discuss a specific challenge, your thought process in breaking it down, and the strategies you employed to arrive at a solution. Focus on your role in the project, how you collaborated with others, and the final outcome.

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What machine learning tools and frameworks are you most familiar with?

List the tools and frameworks you're experienced with, such as TensorFlow, PyTorch, or Scikit-learn. Provide examples of how you've used these technologies in prior roles and how they relate to the Lead Machine Learning Engineer- Data position.

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How do you mentor junior engineers in a technical team?

Explain your mentoring approach, including knowledge sharing, code reviews, and encouraging innovations. Discuss how fostering a supportive learning environment enhances team performance and individual growth.

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What steps do you take to stay updated on emerging technologies in data and AI?

Your answer should showcase your commitment to continuous learning. Mention subscribing to newsletters, participating in webinars, or attending workshops that keep you informed about industry trends and innovations relevant to the Lead Machine Learning Engineer- Data role.

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How do you translate business needs into actionable data solutions?

Detail your communication and analytical strategies for interacting with business stakeholders. Explain your process for understanding their needs and translating them into clear, technical requirements for data solutions.

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What is your experience with synthetic data generation?

Share your knowledge of synthetic data concepts, especially focusing on tools or frameworks you've used for generating data. Understand the implications of using synthetic data for privacy and compliance, which is relevant for the role at Visa.

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How do you prioritize tasks in a fast-paced data environment?

Discuss your task management methodologies and tools you use to prioritize effectively. Explain how you balance short-term deliverables against long-term projects, emphasizing agility and adaptability.

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Full-time, hybrid
DATE POSTED
April 3, 2025

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