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ML Scientist

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

    The ideal candidate will bring the excitement and passion to leverage traditional and generative AI to advance existing fraud detection mechanisms and to innovate and solve new fraud use cases. This engineer will help design, enhance, and build next generation fraud detection solutions in an agile development environment. 

    Essential Functions 

    • Develop new models and re-train existing ones, evaluate performance, and optimize scores. Preferable to have data science knowledge and experience in designing, developing, and implementing Deep Learning methodologies and scalable ML models.

    • Devise deep learning architectures and algorithms for graph-based data, integrating Graph Neural Networks (GNNs) and advanced graph representation learning techniques.

    • Implement efficient methods for monitoring model effectiveness and performance in production.

    • Build ETL pipelines using Spark, Python, HIVE, Scala, or Airflow to process transaction and account-level data, and standardize data fields from various sources.

    • Experiment with and develop custom algorithms for modeling and scoring, utilizing machine learning, data mining, and statistical techniques.

    • Collaborate with Data Scientists, Data Engineers, Software Engineers, and cross-functional partners to design and deploy AI/ML solutions and products.

    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.

    Visa is not offering relocation assistance for this role.

    Qualifications

    Basic Qualifications:
    •Minimum of 6 months of work experience or a Bachelor's Degree

    Preferred Qualifications:
    -2 or more years of work experience
    -Candidates with a PhD or a Master’s degree in a quantitative field, such as
    Statistics, Mathematics, Operational Research, Computer Science, Economics,
    or engineering preferred
    -Experience in Graphical Neural Networks (GNNs), knowledge graphs, and
    graph-based technology and analysis.
    -Strong background in two or more of the following areas: machine learning, AI
    algorithms, computations, statistical learning theory, scalable systems (e.g.
    Spark, Hadoop), large scale data analysis, optimization, functional analysis
    and deep learning.
    -Proficiency in SQL for data extraction and aggregation.
    -Proficiency in Python and deep learning frameworks and libraries such as
    PyTorch and PyG.
    -Skilled in advanced data mining and statistical modeling techniques, including
    predictive modeling, classification, and decision trees.
    -Familiarity with Linux, shell scripting, and commonly used IDEs like Jupyter
    Notebooks.

    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 99,600.00 to 140,700.00 USD 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.

    Visa is not offering relocation assistance for this role.

    Average salary estimate

    $120150 / YEARLY (est.)
    min
    max
    $99600K
    $140700K

    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 ML Scientist, Visa

    At Visa, we're on the lookout for a passionate and innovative ML Scientist to join our team in Foster City, CA. If you're excited about harnessing the power of traditional and generative AI to enhance fraud detection methods, this is the perfect opportunity for you! In this role, you'll dive deep into developing and optimizing machine learning models, specifically focusing on reinventing fraud detection solutions in our agile environment. Collaborating with Data Scientists, engineers, and other cross-functional partners, you'll help create advanced algorithms and architectures that make a significant impact. You'll be responsible for building ETL pipelines, implementing effective monitoring strategies, and experimenting with cutting-edge technologies such as Graph Neural Networks (GNNs). The ideal candidate should have a knack for data science and a robust understanding of deep learning methodologies. Join us to contribute to shaping a reliable and secure payments network that uplifts everyone. Don't miss the chance to be part of a vibrant company that stands at the forefront of technology and innovation!

    Frequently Asked Questions (FAQs) for ML Scientist Role at Visa
    What are the main responsibilities of an ML Scientist at Visa?

    As an ML Scientist at Visa, your key responsibilities will involve developing new and optimizing existing machine learning models, focusing on fraud detection mechanisms. You'll design deep learning architectures, build efficient ETL pipelines, and implement monitoring systems for model performance. Collaborating with a talented team, you'll innovate and develop AI/ML solutions and products to enhance overall business effectiveness.

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    What qualifications are needed to become an ML Scientist at Visa?

    Visa prefers candidates with a Master's or PhD in a quantitative field, such as Computer Science or Statistics. Additionally, having 2 or more years of work experience in machine learning, AI algorithms, and proficiency in tools like Python and SQL are desirable. A solid foundation in deep learning frameworks and familiarity with GNNs will set you apart as an ideal candidate.

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    How does Visa support the professional growth of its ML Scientist?

    At Visa, we believe in nurturing talent and providing ample opportunities for professional development. As an ML Scientist, you will have access to continuous training programs, mentorship from experienced professionals, and opportunities to work on innovative projects. Our collaborative environment encourages knowledge sharing, which can significantly enhance your skills and accelerate your career growth.

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    What tools and technologies will an ML Scientist at Visa be expected to use?

    As an ML Scientist at Visa, you will regularly work with technologies like Python, Spark, and deep learning frameworks including PyTorch. Familiarity with SQL for data extraction and tools for visualizing and analyzing data is essential. Understanding graph-based technology and libraries for implementing Graph Neural Networks (GNNs) will also be part of your toolkit.

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    What is the work culture like for an ML Scientist at Visa?

    Visa prides itself on a collaborative and innovative work culture. As an ML Scientist, you'll enjoy a hybrid work model that allows flexibility between remote and in-office work. The environment is driven by teamwork, where cross-functional partnerships are encouraged to foster creative solutions and drive impactful results.

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    Common Interview Questions for ML Scientist
    Can you explain your experience with machine learning algorithms and their application?

    In responding to this question, outline specific machine learning projects you’ve worked on, the algorithms used, and the outcomes achieved. Emphasize any practical applications relevant to fraud detection if possible, showcasing your ability to translate theoretical knowledge into real-world solutions.

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    Describe a challenging problem you've solved using deep learning techniques.

    Provide a detailed example of a complex problem involving deep learning. Discuss your approach, the models you implemented, and the results you achieved. Highlight your problem-solving skills and ability to adapt methodologies to suit project needs.

    Join Rise to see the full answer
    How would you approach the development of a fraud detection model?

    When answering, outline a systematic approach including data collection, cleaning, feature engineering, model selection, training, and evaluation. Mention your experience with specific tools or techniques that could enhance the model's performance, and emphasize your focus on accuracy and reliability.

    Join Rise to see the full answer
    What is your understanding of Graph Neural Networks and when would you use them?

    Explain your understanding of GNNs specifically in relation to processing graph data. Discuss scenarios in which GNNs provide a clear advantage, such as fraud detection involving complicated relational data. This shows your technical expertise and relevant knowledge.

    Join Rise to see the full answer
    How do you stay updated with new technologies in machine learning?

    In your response, mention various resources such as online courses, webinars, and conferences you attend for ongoing learning. Highlight subscriptions to leading journals or participation in forums that emphasize your proactive approach to staying informed about new trends and techniques.

    Join Rise to see the full answer
    Can you describe your experience with big data technologies?

    Discuss specific technologies you have used, such as Spark, Hadoop, or any data processing frameworks. Provide examples of projects where you employed these technologies and how they contributed to the success of your machine learning initiatives.

    Join Rise to see the full answer
    How do you evaluate the performance of your models?

    Detail the metrics and techniques you use to evaluate model performance, such as accuracy, precision, recall, F1 score, or ROC-AUC. Discuss how you would use these metrics in the context of fraud detection, underlining the importance of minimizing false positives and negatives.

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    What role does collaboration play in your data science projects?

    Explain the importance of teamwork in your work. Share experiences where collaboration with cross-functional teams, such as software engineers or product managers, led to better insights or faster project completion, reinforcing your ability to work effectively in a team environment.

    Join Rise to see the full answer
    How do you handle model deployment in production environments?

    Discuss your approach to deploying models into production, focusing on challenges faced and solutions implemented. Mention any tools or methodologies used for version control, monitoring, and performance tracking, emphasizing the important steps to ensure smooth deployment.

    Join Rise to see the full answer
    What motivates you to work in the field of machine learning?

    Reflect on your passion for technology and your desire to impact positively through machine learning. Share relatable anecdotes about your career journey that connect your skills with the mission of using technology to solve real-world problems like fraud detection, motivating your interest in the role.

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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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    DATE POSTED
    January 11, 2025

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