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Data Scientist (Visa Predictive Models) - job 31 of 33

Visa has the world’s largest consumer payment transaction dataset.  We see data on over 250 billion transactions every year from all over the world. We use that data to help our clients in the payment ecosystem grow their businesses and to help consumers access a fast, safe, and rewarding payment experience. Visa Predictive Modeling (VPM) team develops and maintains predictive machine learning models to primarily support Visa Risk and Identity Solutions. Using VisaNet data and leveraging Machine Learning (ML) and Artificial Intelligence (AI), our model scores help Visa clients all over the world for fraud defense, identity verification, smart marketing, etc. Through our models and services, VPM fuels the growth of Visa clients, generates, and diversifies revenues for VISA, while improving Visa Card customer experience and their financial lives.

Within VPM, the Acceptance Risk Model Team is responsible for developing real-time fraud detection models serving merchants. We leverage a set of rich data available at merchant check-out including transactional, digital and identity information to detect and stop fraud.

This is a Technical (Individual Contributor) role.  Your responsibilities include:

  • Building and validating predictive models with advanced machine learning techniques and tools to drive business value, interpreting, and presenting modeling and analytical results to non-technical audience.
  • Conducting research using latest and emerging modeling technologies and tools (e.g., Deep Neural Networks, RNN, LSTM, etc.) to solve new fraud detection business problems.
  • Improving the modeling process through MLOps and automation to drive efficiency and effectiveness.
  • Partnering with a cross functional team of Product Managers, Data Engineers, Software Engineers, and Platform Engineers to deploy models and/or model innovations into production.
  • Managing model risks in line with Visa Model Risk Management requirements.
  • Conducting modeling analysis to address internal and external clients’ questions and requests.

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.

Average salary estimate

$100000 / YEARLY (est.)
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max
$85000K
$115000K

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What You Should Know About Data Scientist (Visa Predictive Models), Visa

At Visa, we're excited to welcome a Data Scientist to our dynamic team in Washington. With a treasure trove of consumer payment transaction data, spanning over 250 billion transactions every year, the insights we gather help shape the future of payments. As part of the Visa Predictive Modeling (VPM) team, you'll be at the heart of innovation, developing and maintaining cutting-edge predictive machine learning models that bolster Visa Risk and Identity Solutions. Your work will contribute directly to essential functions like fraud detection, identity verification, and enabling smart marketing strategies for our clients worldwide. On a daily basis, you’ll employ advanced machine learning techniques, delve into research, and explore state-of-the-art technologies such as Deep Neural Networks and LSTM to tackle complex fraud detection challenges. Collaboration is key here, as you'll partner with a diverse group of Product Managers, Data Engineers, and Software Engineers to seamlessly deploy your models into production. Plus, you'll help enhance the modeling process through MLOps and automation to maximize efficiency. This role requires managing model risks in compliance with Visa’s standards while also addressing queries from clients. And, with the flexibility of a hybrid position, you’ll enjoy a blend of remote work and in-office teamwork, creating a balanced professional environment. If you're ready to make a real impact in the payment landscape, we'd love to see you join us at Visa as a Data Scientist.

Frequently Asked Questions (FAQs) for Data Scientist (Visa Predictive Models) Role at Visa
What are the key responsibilities of a Data Scientist at Visa?

As a Data Scientist at Visa, your main responsibilities will include building and validating predictive models using advanced machine learning techniques, conducting research on emerging modeling technologies, enhancing processes through MLOps and automation, collaborating with cross-functional teams to deploy models, and managing model risks in line with Visa’s Model Risk Management requirements.

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What qualifications are needed for the Data Scientist position at Visa?

To be successful as a Data Scientist at Visa, candidates typically need a strong background in machine learning, data analysis, and statistical modeling. A relevant degree in Computer Science, Data Science, Statistics, or a related field is often required, along with experience in model development and a good understanding of fraud detection mechanisms.

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How does the Data Scientist role at Visa contribute to fraud detection?

The Data Scientist role at Visa directly contributes to fraud detection by developing predictive models that assess risks in real-time at merchant check-out. Using various data points, including transactional and identity information, the models help in identifying and mitigating fraudulent activities, which are crucial for maintaining client trust and ensuring secure transactions.

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What technologies and tools do Visa Data Scientists use?

Data Scientists at Visa utilize a variety of advanced machine learning tools and technologies, including Deep Neural Networks, recurrent neural networks like LSTM, R, Python, and MLOps practices to ensure efficient model development, deployment, and monitoring. Staying current with the latest innovations in the field is also essential for success.

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Is the Data Scientist position at Visa hybrid or remote?

Yes, the Data Scientist position at Visa is a hybrid role, which means you'll have the flexibility to alternate between working remotely and in the office. Employees typically work from the office 2-3 days a week, based on business needs and leadership decisions, offering a great balance between collaboration and independent work.

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Common Interview Questions for Data Scientist (Visa Predictive Models)
Can you explain your experience with machine learning algorithms?

When answering this question, focus on specific algorithms you’ve used, such as linear regression or neural networks. Discuss projects where you implemented these algorithms, the results achieved, and what you learned. This showcases your practical knowledge and ability to apply theoretical concepts.

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How do you approach feature selection in your models?

Discuss techniques like correlation analysis, PCA, or feature importance ranking. Emphasize the importance of domain knowledge and how it influences your choices. Providing a specific example of a project where you improved model performance through effective feature selection can be compelling.

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Tell me about a challenging data-related problem you solved.

Choose a specific example that illustrates your problem-solving skills. Explain the challenge, the steps you took to analyze the data, the solution you implemented, and the impact it had on the project or team. This highlights your analytical and critical thinking abilities.

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How do you validate your predictive models?

Discuss the importance of validation techniques such as cross-validation, A/B testing, and performance metrics like ROC-AUC or F1 Score. Be prepared to explain how you use these methods to ensure your models are robust and reliable before deployment.

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Can you describe your experience with MLOps?

Detail your familiarity with MLOps principles, including CI/CD for machine learning, automation in model deployment, and monitoring. Share any tools or frameworks you’ve used for MLOps, and how implementing these practices improved model efficiency or reduced time-to-market.

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What strategies do you use to communicate complex data findings to non-technical stakeholders?

Highlight your ability to break down complex concepts into simple terms, using visual aids or storytelling techniques. Sharing an instance where you successfully presented data findings to non-technical audiences can demonstrate your communication skills effectively.

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How do you stay current with advancements in data science and machine learning?

Talk about the resources you use, such as online courses, webinars, industry conferences, or publications. Demonstrating your commitment to ongoing learning and professional development is valued in fast-evolving fields like data science and machine learning.

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What is your experience with real-time data processing?

Describe any projects or systems you've worked with that involve real-time data processing, focusing on the technologies you used (like Apache Kafka or Spark). Discuss the challenges you faced and how you overcame them to deliver value from real-time insights.

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How do you handle data quality issues in your work?

Explain your approach to identifying and addressing data quality problems, whether through data cleaning, transformation processes, or use of validation checks. Providing a specific example helps illustrate your ability to maintain high standards in data integrity.

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Why do you want to work at Visa as a Data Scientist?

This is your opportunity to show your knowledge of Visa’s values and goals. Explain what excites you about working for Visa, such as their commitment to innovation in payment solutions, and how your skills align with their mission to enhance customer experience through data insights.

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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 15, 2025

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