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DIRECTOR, DATA SCIENCE \U ML US A2A PAYMENTS \U OPEN BANKING

Descrição da oferta de emprego

Job Description As a Director Data Science & Machine Learning US A2A Payments & Open Banking, you will partner closely with the VP Risk and other sales and product leaders to shape the risk & ML strategy of Visa' A2A payments and open banking products in North America.
You will build a world-class data science team to execute on the strategy and bring it to life in day-to-day risk operations.
he right candidate will possess strong data science and machine learning background, with demonstrated experience in building, training, implementing and optimizing advanced ML models for payments.
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' business and drive solutions that will enhance the safety and integrity of Visa' account to account payment ecosystem.
This role represents an exciting opportunity to make key contributions to a strategic offering for Visa.
The candidate will be a self-starter comfortable with ambiguity, with strong attention to detail, and excellent collaboration skills.
o be successful in this role, you need to be a self-starter, highly organized, and deeply understand the mindset of consumers and the structure of globally organized fraudsters.
You will need to be able to build a brand-new team and hire top-tier data science talent that fits within the culture.
You will also need to be able to successfully handle stakeholders on all levels across the organization, in a global environment.
esponsibilitiesBuild, own, and manage the data science & machine learning team for Visa' North America Account to Account payments and open banking teamBuild and manage a global team of top-tier data science talent with diverse backgrounds to execute on Visa' product strategy in the A2A payments spaceDrive key initiatives and improvements together with product and engineering to meaningfully reduce the loss exposure of the businessPartner with product, technology, sales, marketing, finance and other functions to identify and execute on key risk initiatives to limit our exposure to fraud and settlement riskUnderstand the broad set of Visa' data science and machine learning capabilities and products and identify opportunities for collaboration and creating unique advantagesAlign with product teams on risk requirements and support the product and engineering teams to execute on the aligned roadmapBuild strong relationships with key stakeholders internally and externally to execute with excellence and align closely with Visa' risk teamResponsible for driving data science & machine learning requirements, process design, and innovation (such as big data, artificial intelligence, machine learning, graph databases)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 Qualifications10 or more years of relevant work experience with a Bachelor Degree or at least 8 years of experience with an Advanced Degree (e.
.
Masters, MBA, JD, MD) or 5 years of work experience with a PhD Preferred Qualifications7-10 or more years\u experience in a leadership role in Data Science with track record of building excellent data science & ML teamsHands-on experience building high-performing data science & ML teams from the ground up in the payments space with a proven track record of successful business impact at scaleSignificant experience building rules & models to counter fraud, and manage settlement and credit risk for consumers and businesses utilizing real-time open banking data (account, identity, balance, and activity data) to manage risk and prevent losses from ACH and EFT paymentsIn-depth knowledge of payment products, rails, and functionality including the associated risks in North America (ACH, RTP, FedNow, EFT, etc.)Experience constructing features utilizing open-banking data, in-house data, and third-party data to enhance rules and modelsFamiliarity with techniques used to model rare events is essentialAbility to strike a balance between net loss management utilizing all tools available while maintaining industry leading approval ratesStrong passion for data science, machine learning, and the payments space and open bankingWorking well within a heavily matrixed organizations, multi-national experience a plusPrior experience building and managing the data science & ML team in payments is a mustStrategic thinking, thought leadership, teamwork, and relationship-building skillsOutstanding communication and presentation skills at all levelsExperience in developing large scale, enterprise class distributed systems of high availability, low latency, & strong data consistencyExperience in working with agile lifecycle and/or tracking and process management tools, e.
., JIRAExperience in architecting and implementing solutions with Continuous Integration and Continuous Delivery as well as automated model retraining in mindExperience with data visualization and business intelligence tools like Tableau and QuickSightAbility to fully understand technical architecture, APIs and overall system designHigh level of competence in Python, Spark, and equivalent tools Additional Information 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.
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Detalhes da oferta

Empresa
  • VISA
Localidade
  • Em todo Portugal
Endereço
  • Indeterminado - Indeterminado
Tipo de Contrato
  • Indeterminado
Data de publicação
  • 08/11/2024
Data de expiração
  • 06/02/2025
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