Lead Machine Learning Scientist

Key Skills

Apache Hadoop, Apache Hive, Apache Pig, Big Data Technology, Data Mining, Data Visualization, Deep Learning, Linux Operating system, Machine learning techniques, Natural Language Processing (NLP), Python Programming, SAS, Scala Programming, Spark Core, SQL, Tableau, TensorFlow, Unix Operating system

Job Description

This position is ideal for an experienced ML engineer who is passionate about collaborating with business and technology partners in solving challenging fraud prevention problems. You will be a key driver in the effort to define the shared strategic vision for the Payment Systems Risk platform and defining tools and services that safeguard Visa’s payment systems.

The right candidate will possess strong ML and Data Science background, with demonstrated experience in building, training, implementing and optimized advanced AI models for payments, risk or fraud prevention products that created business value and delivered impact within the payments or payments risk domain or have experience building AI/ML solutions for similar industries.

A successful candidate is a technical leader with the ability to engage in high bandwidth conversations with business and technology partners and be able to think broadly about Visa’s business and drive solutions that will enhance the safety and integrity of Visa’s payment ecosystem. The candidate will help deliver innovative insights to Visa's strategic products and business. This role represents an exciting opportunity to make key contributions to strategic offering for Visa. This candidate needs to have strong academic track record and be able to demonstrate excellent software engineering skills. The candidate will be a self-starter comfortable with ambiguity, with strong attention to detail, and excellent collaboration skills.

Essential Functions

• As a Lead ML Scientist in Payment Systems Risk team, you will help design, enhance, and build next generation fraud detection solutions in an agile development environment
• Formulate business problems as technical data problems while ensuring key business drivers are captured in collaboration with product stakeholders.
• Work with software engineers to ensure feasibility of solutions. Deliver prototypes and production code based on need.
• Experiment with in-house and third-party data sets to test hypotheses on relevance and value of data to business problems.
• Build needed data transformations on structured and un-structured data.
• Build and experiment with modeling and scoring algorithms. This includes development of custom algorithms as well as use of packaged tools based on machine learning, data mining and statistical techniques.
• Devise and implement methods for adaptive learning with controls on effectiveness, methods for explaining model decisions where necessary, model validation, A/B testing of models.
• Devise and implement methods for efficiently monitoring model effectiveness and performance in production.
• Devise and implement methods for automation of all parts of the predictive pipeline to minimize labor in development and production.
• Contribute to development and adoption of shared predictive analytics infrastructure
• Mentor and train other ML scientists on the team on key solutions
• Able to work on multiple projects and initiatives with different/competing timelines and demands.
• Present technical solutions, capabilities, considerations, and features in business terms. Effectively communicate status, issues, and risks in a precise and timely manner

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 three days a week, Tuesdays, Wednesdays and Thursdays with a general guidepost of being in the office 50% of the time based on business needs

Qualifications

Basic Qualifications:

• 10 or more years of work experience with a Bachelor’s Degree or at least 8 years of work experience with an Advanced Degree (e.g. Masters/ MBA/JD/MD) or at least 3 years of work experience with a PhD
• PhD in Computer Science, Operations Research, Statistics, or highly quantitative field (or equivalent experience) with strength in Deep Learning, Machine Learning, Data Mining, Statistical or other mathematical analysis
• Relevant coursework in modeling techniques such as logistic regression, Naïve Bayes, SVM, decision trees, or neural networks
• Expert in leading-edge areas such as Machine Learning, Deep Learning, Stream Computing and MLOps
• Ability to program in one or more scripting languages such as Perl or Python and programming languages such as Java or Scala
• Experience with one or more common statistical tools such SAS, R, KNIME, Matlab
• Excellent understanding of algorithms and data structures.
• Excellent analytic and problem-solving capability combined with ambition to solve real-world problems
• Excellent interpersonal, facilitation, and effective communication skills (both written and verbal) and the ability to present complex ideas in a clear, concise way
• Have great work ethics, and be a team player striving to bring the best results as a team
• Collaborate across engineering teams and leaders in Payments Risk, Visa Research, AI Platform, Operations, and Infrastructure (O&I), security and platform teams

Preferred Qualifications:

• 12 or more years of work experience with a Bachelor’s Degree or 8-10 years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 6+ years of work experience with a PhD
• PhD degree in Computer Science or related field and 10+ years of Machine Learning System Development Experience after PhD
• High level of competence in Python, Spark, and Unix/Linux scripts
• Real world experience using Hadoop and the related query engines (Hive / Impala)
• Extensive experience with SAS/SQL/Hive for extracting and aggregating data
• Experience working with large datasets using tools like Pig or Hive is a plus
• Experience with Big Data and analytics leveraging technologies like Hadoop, Spark, Scala, and MapReduce
• Deep learning experience with TensorFlow is a plus
• Experience with Natural Language Processing is a plus
• Publications or presentation in recognized Machine Learning and Data Mining journals/conferences is a plus
• Experience with data visualization and business intelligence tools like Tableau
• Modeling experience in card industry or financial service company using for fraud, credit risk, payments is plus
• Proficiency in designing & solving classification/prediction problems using open-source libraries such as Scikit learn
• Experience in developing large scale, enterprise class distributed systems of high availability, low latency, & strong data consistency
• Experience developing instrumentation for software components, to help facilitate real-time and remote troubleshooting/performance monitoring
• Experience in architecting solutions with Continuous Integration and Continuous Delivery in mind
• Familiarity with in distributed in-memory computing technologies

U.S. APPLICANTS ONLY: The estimated salary range for a new hire into this position is 150,900 to 196,200 USD, 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.