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Staff Data Scientist (Visa Predictive Models)

Company Description

Visa is a world leader in payments and technology, with over 259 billion payments transactions flowing safely between consumers, merchants, financial institutions, and government entities in more than 200 countries and territories each year. Our mission is to connect the world through the most innovative, convenient, reliable, and secure payments network, enabling individuals, businesses, and economies to thrive while driven by a common purpose – to uplift everyone, everywhere by being the best way to pay and be paid.

Make an impact with a purpose-driven industry leader. Join us today and experience Life at Visa.

Job Description

Visa has the world’s largest consumer payment transaction dataset.  We see data on over 200 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 like enumeration attacks and/or improve existing production models’ performance.

  • 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.

Qualifications

Basic Qualifications

  • 5 or more years of relevant work experience with a Bachelors Degree or at least 2 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 0 years of work experience with a PhD


Preferred Qualifications

  • 6 or more years of work experience with a Bachelor’s degree or 4 or more years of relevant experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or up to 3 years of relevant experience with a PhD in a quantitative or engineering field.
  • 3 or more years of experience with modern machine learning framework and tools like Gradient Boosting (e.g., XGBoost), and modern Deep Neural Network framework (e.g. RNN, LSTM, Transformer) and tools (e.g., PyTorch, TensorFlow).
  • 3 or more years of experience with production model development, implementation, and support.
  • Experience with Payment Fraud models.
  • Experience in Agile Development and tools.
  • Proven ability to quickly learn and apply new tools and techniques.
  • A strong innovation leader yet a practitioner to create tangible business value balancing business objectives and technological constraints.
  • Must be a team-player and capable of handling multi-tasks in a dynamic environment.
  • Excellent business writing, verbal communication, and presentation skills to technical and non-technical audiences.

Technical Qualifications

  • Proficiency in Python, Hadoop, Hive, Spark for big data analysis and modeling
  • Experience with script and shell programming in Unix/Linux
  • Experience with using GitHub and Jira for data science projects

Additional Information

Work Hours: Varies upon the needs of the department.

Travel Requirements: This position requires travel 5-10% of the time.

Mental/Physical Requirements: This position will be performed in an office setting.  The position will require the incumbent to sit and stand at a desk, communicate in person and by telephone, frequently operate standard office equipment, such as telephones and computers.

Visa is an EEO Employer.  Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status.  Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with EEOC guidelines and applicable local law.

Visa will consider for employment qualified applicants with criminal histories in a manner consistent with applicable local law, including the requirements of Article 49 of the San Francisco Police Code.

U.S. APPLICANTS ONLY: The estimated salary range for a new hire into this position is 139,800.00 to 202,750.00 per year, which may include potential sales incentive payments (if applicable). Salary may vary depending on job-related factors which may include knowledge, skills, experience, and location. In addition, this position may be eligible for bonus and equity. Visa has a comprehensive benefits package for which this position may be eligible that includes Medical, Dental, Vision, 401 (k), FSA/HSA, Life Insurance, Paid Time Off, and Wellness Program.

Average salary estimate

$171275 / YEARLY (est.)
min
max
$139800K
$202750K

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 Staff Data Scientist (Visa Predictive Models), Visa

Are you ready to tackle big challenges with big data? Look no further than the Staff Data Scientist position at Visa in Washington, DC! In this exciting role within Visa's Predictive Modeling team, you'll be at the heart of transforming massive datasets into actionable insights that shape the future of payments. Here at Visa, our mission is to uplift everyone, everywhere through the power of technology, and as a Staff Data Scientist, you'll play a crucial part in this journey. Your main responsibility will involve building and validating cutting-edge predictive models using advanced machine learning techniques. You’ll have the chance to conduct innovative research and solve new fraud detection problems that face our clients worldwide. By collaborating with talented teams of product managers, data engineers, and software engineers, you'll deploy model innovations and efficiently manage model risks. We thrive on a culture of teamwork and creativity, where your insights can directly impact millions of users. With our hybrid work model, you can enjoy the flexibility of remote work while still engaging with your team in the office. If you bring a strong background in machine learning tools, excellent communication skills, and a passion for making a difference through data-driven methods, apply today and help us shape the future of secure payments at Visa!

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

As a Staff Data Scientist at Visa, your primary responsibilities will include building and validating predictive models that leverage advanced machine learning techniques to drive business value. This entails interpreting and presenting analytical results to a non-technical audience, conducting research into emerging modeling technologies, and improving model performance. You'll work closely with cross-functional teams to deploy models into production, manage model risks, and conduct analyses to address client queries and requests.

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What qualifications are needed for a Staff Data Scientist position at Visa?

To qualify for the Staff Data Scientist role at Visa, candidates should ideally have at least 6 years of work experience with a Bachelor’s degree, or related experience with an Advanced degree. A strong foundation in machine learning frameworks, production model development, and experience with payment fraud models is preferred. Proficiency in programming languages and tools such as Python, Hadoop, and Spark is also essential.

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What machine learning tools are used by the Staff Data Scientist at Visa?

Staff Data Scientists at Visa utilize a variety of modern machine learning frameworks and tools, including Gradient Boosting models like XGBoost, and neural network frameworks like RNNs and LSTMs. They also employ tools such as PyTorch and TensorFlow to address complex modeling challenges and enhance existing models.

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

Visa offers a hybrid work environment for Staff Data Scientists, allowing team members the flexibility to work both remotely and in the office. Employees are expected to be in the office 2-3 days a week, fostering collaboration and innovation while accommodating personal working preferences.

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How does a Staff Data Scientist contribute to fraud prevention at Visa?

In the Staff Data Scientist role at Visa, you will contribute to fraud prevention by developing and refining predictive models that identify and mitigate fraudulent transactions. By analyzing large datasets and leveraging advanced machine learning techniques, you'll help improve the security of transactions for Visa clients across the globe.

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Common Interview Questions for Staff Data Scientist (Visa Predictive Models)
Can you describe your experience with building predictive models?

When answering this question, focus on specific projects where you've developed predictive models. Highlight tools and techniques you used, such as machine learning frameworks or data sources. Discuss the impact your models had on business decisions or outcomes, and emphasize your approach to model validation.

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What machine learning algorithms are you most familiar with, and why?

In your response, mention popular algorithms like Decision Trees, Random Forests, Gradient Boosting, and Neural Networks. Elaborate briefly on the scenarios in which each algorithm excels, and provide examples from your work experience to underscore your understanding and practical application.

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How do you ensure the models you develop are scalable?

To ensure scalability, detail your experience with model optimization and efficiency. Discuss the importance of using robust infrastructure, tools like Apache Spark, and best practices in MLOps to maintain model performance as data volume increases. Provide examples of how you've implemented scalability in past projects.

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

In your answer, walk the interviewer through a specific data challenge you encountered. Describe the context, the steps you took to analyze the data, the solutions you explored, and the outcome. This showcases your problem-solving skills and ability to think critically.

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How do you approach presenting complex data findings to non-technical stakeholders?

Highlight your ability to translate data science concepts into layman's terms. Discuss specific strategies you use, such as visualizations, analogies, and focusing on the business implications of the findings rather than technical jargon. This showcases your communication skills.

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What steps do you take to validate your models?

Recount your methodology for model validation, discussing techniques like cross-validation, A/B testing, and the importance of performance metrics. Emphasize your commitment to continuous improvement and how you adjust models based on validation results.

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Can you explain your experience with Agile methodologies?

Share your experience working in Agile teams, focusing on practices like sprints, scrums, and collaborating with cross-functional teams. Explain how Agile principles have positively impacted the speed and quality of your work in data science.

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What strategies do you use for effective collaboration with cross-functional teams?

Discuss the importance of clear communication and establishing a common understanding of goals. Share examples of how you've worked with product managers, engineers, and other stakeholders to create a collaborative environment that fosters creativity and efficient problem-solving.

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How do you stay updated on the latest trends in data science and machine learning?

Mention resources you use, such as online courses, webinars, journals, and practitioner blogs. Talk about your involvement in data science communities or conferences, demonstrating your commitment to lifelong learning and professional growth in the field.

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

Clearly articulate your motivation for wanting to join Visa, mentioning the company’s reputation, its mission-driven culture, and how you’re excited about leveraging advanced data techniques in the payments industry. Personalize this response by linking it to your own values and career aspirations.

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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
December 16, 2024

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