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Data Scientist (Visa Predictive Models) - job 33 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

$105000 / YEARLY (est.)
min
max
$90000K
$120000K

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 Data Scientist (Visa Predictive Models), Visa

Join Visa as a Data Scientist specializing in Predictive Models and dive into the exciting world of data analytics! Based in Washington, you will collaborate with a talented team to tap into the world’s largest consumer payment transaction dataset, which includes insights from over 250 billion transactions each year. At Visa, we harness this data to empower our clients in the payment ecosystem, enhancing their businesses while providing a seamless and secure payment experience for consumers. As part of the Visa Predictive Modeling (VPM) team, you’ll develop and maintain cutting-edge predictive machine learning models to support Visa Risk and Identity Solutions. In this role, your expertise in machine learning and artificial intelligence will be crucial to creating solutions for fraud defense, identity verification, and more. You’ll have the chance to build and validate models, work with the latest technologies like Deep Neural Networks and LSTM, and contribute to enhancing our modeling process through MLOps. Working collaboratively with Product Managers, Data Engineers, and Software Engineers, you’ll play an essential role in deploying innovative models to production. This hybrid position allows you to balance remote work with in-office collaboration 2-3 days a week, fostering a cohesive team environment while adapting to your work preferences. If you’re passionate about data science and eager to make a significant impact, Visa is the place for you!

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

As a Data Scientist at Visa, your key responsibilities include building and validating predictive models using advanced machine learning techniques, conducting research on emerging modeling technologies to address fraud detection challenges, and improving the modeling process through MLOps. You'll also partner with cross-functional teams to deploy these models into production and manage model risks as per Visa's Model Risk Management requirements.

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What qualifications are needed to be a Data Scientist at Visa?

To qualify for the Data Scientist position at Visa, candidates typically need a Master's degree or higher in a quantitative field such as Statistics, Mathematics, or Computer Science. Proficiency in machine learning techniques and tools, as well as experience in programming languages like Python or R, are crucial. Strong analytical skills, along with the ability to communicate complex concepts to a non-technical audience, are also essential.

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How does Visa use data for fraud detection as a Data Scientist?

Visa uses its extensive payment transaction data to create predictive models that identify and mitigate fraud risks. As a Data Scientist, you'll leverage various data points available at the merchant checkout, including transactional, digital, and identity information, to develop real-time fraud detection models that protect both merchants and customers, ensuring a secure payment environment.

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What technologies should a Data Scientist at Visa be familiar with?

As a Data Scientist at Visa, familiarity with advanced machine learning techniques, such as Deep Neural Networks, Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) networks is important. Additionally, being adept with programming languages such as Python or R and tools for MLOps will enhance your effectiveness in this role.

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What is the work environment like for a Data Scientist at Visa?

The work environment for a Data Scientist at Visa is both dynamic and collaborative. With this hybrid position, you'll have the flexibility to work remotely while also engaging in in-person teamwork 2-3 days a week. The team culture emphasizes innovation, data-driven decision-making, and continuous learning, ensuring you’re well-supported and involved in exciting projects.

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Common Interview Questions for Data Scientist (Visa Predictive Models)
What machine learning techniques do you find most effective for fraud detection?

In my experience, techniques such as Random Forest, Gradient Boosting, and Neural Networks are particularly effective for fraud detection. These methods can handle the complex relationships in the data and improve accuracy. I would emphasize the importance of selecting the right technique based on the specific data context and business problem.

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How do you validate the predictive models you develop?

I validate predictive models using techniques such as cross-validation, testing against historical data, and evaluating the model's performance metrics like precision, recall, and the F1 score. It’s essential to ensure that the models generalize well to new, unseen data to effectively mitigate fraud risks.

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Can you describe a project where you applied machine learning to solve a real-world problem?

One project involved developing a machine learning model to identify fraudulent transaction patterns. I used a combination of supervised learning techniques and collaborated with cross-functional teams to refine the model based on feedback. The successful deployment of this model led to a significant reduction in fraudulent transactions.

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What role does data preprocessing play in your modeling approach?

Data preprocessing is crucial; it involves cleaning, transforming, and normalizing the data to ensure accuracy and performance of the models. I usually spend a considerable amount of time on this step, as proper preprocessing can significantly influence the outcome and robustness of the predictive models.

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How do you approach communicating your findings to non-technical stakeholders?

I focus on translating technical results into relevant business insights by using clear visualizations and straightforward language. I aim to highlight the implications of the findings and recommendations in a way that aligns with their strategic goals, ensuring that the technical aspects are accessible and actionable.

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What experience do you have with MLOps?

I have hands-on experience with MLOps, including using tools for automating machine learning workflows, monitoring model performance, and deploying models into production. This has allowed me to improve efficiency and reliability in the modeling processes, enabling continuous integration and delivery of model updates.

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What are the critical risks you consider while developing predictive models?

When developing predictive models, I consider risks such as overfitting, data quality issues, and model bias. I ensure to implement validation checks and regularly review the model’s performance to mitigate these risks and ensure that they remain robust and fair.

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How do you stay updated on the latest advancements in machine learning?

I regularly attend conferences, participate in webinars, and engage with online communities. Reading research papers and articles on platforms like arXiv and Medium also helps me keep abreast of the latest techniques and trends in machine learning.

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Can you discuss your experience with deep learning frameworks?

I have experience using frameworks such as TensorFlow and PyTorch for developing and training deep learning models. These tools are invaluable for building complex architectures and experimenting with various model configurations to find the optimal solution for our predictive tasks.

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What do you consider when choosing features for your models?

When selecting features, I consider their relevance to the target variable, correlation with other features, and potential for improving model predictive power. I also employ techniques such as feature importance assessment and recursive feature elimination to streamline the feature selection process.

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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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