Põhiline

Sr. Data Scientist US A2A Payments & Open Banking

Lisboa, Lisbon Metropolitan Area, Portugal Ettevõte: Jobio Klient / Tööandja: Visa
Postitatud: 18.05.2026
Sulgemiskuupäev: 02.07.2026
Tööviide: 4c7e6ca91909ea91a7afcaf50ea450bf

Tööinfo

Asukoht
Lisboa, Lisbon Metropolitan Area, Portugal
Ettevõte
Jobio
Klient / Tööandja
Visa
Tööviide
4c7e6ca91909ea91a7afcaf50ea450bf
Kuulutuse tüüp
Põhiline
EL-i tööluba nõutav
Ei
Postitatud
18.05.2026
Sulgemiskuupäev
02.07.2026

Tööülesannete kirjeldus

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

As a Sr. Data Scientist, you will report to the Sr. Director of Data Science & Machine Learning and partner with risk, engineering and product to provide cutting-edge decision science to A2A payment risk. The 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.

The successful candidate will have experience in risk management for payments, preferably in open banking, and a solid understanding of both fraud and credit risk. They will be able to partner with product and engineering teams to scope solutions to deliver real time transaction decisioning. 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.

Responsibilities

  • Be an out-of-the-box thinker who is passionate about brainstorming innovative ways to use data to manage risk in open banking

  • Use predictive modeling and mine data from company databases and/or open banking sources to decrease payment losses while optimizing the consumer and merchant experience

  • Assess the effectiveness and accuracy of new data sources and data gathering techniques both from external data and across the Visa network

  • Extract and understand data to improve the understanding of risk and develop visualizations to make your complex analyses accessible to a broad audience

  • Deep understanding of fraud patterns and how to systematically detect and stop fraudsters

  • Develop processes and tools to monitor and analyze model performance and data accuracy including control groups, decline inference techniques, and run time optimization

  • Partner with product and engineering to identify improvements that will reduce the loss exposure or improve the customer experience of A2A Payments

  • Support sales and account management in a consultative manner on optimizing and communicating the risk strategies for individual merchants

  • Build strong relationships with key stakeholders at the working level to execute with excellence and align closely with Visa’s risk team

This is a hybrid position. Expectation of days in office will be confirmed by your hiring manager.

Qualifications

Basic Qualifications

  • 8 years of relevant work experience with a Bachelor Degree or 5 years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 2 years of work experience with a PhD



Preferred Qualifications

  • 9+ years of relevant work experience and a Bachelor’s Degree or 7 or more relevant years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 3+ years of experience with a PhD
  • Experience in payment risk management is highly preferred and experience in open banking either in North America, United Kingdom, and/ or Europe (e.g. ACH, BACS, Faster Payments, RTP, SEPA) would be extremely helpful with preference given to US experience
  • Experience with data mining and statistical modeling such as regression, clustering techniques, decision trees, etc. is required and application to payment and/or risk use cases is preferred
  • High level of competence in SQL, Python, Spark/Scala, and Unix/Linux scripts
  • Real world experience using Hadoop and the related query engines (Hive /Impala) for big data processing
  • Ability to construct model features utilizing open-banking data, in-house data,and/or third-party data to enhance rules and models
  • Experience utilizing models and developing features for real time graphicaltools and with highly complex networks is highly desired
  • Exposure to creating requirements in partnership with product and engineering teams for data science infrastructure used in real-time transaction decisioning
  • Comfort in working with agile lifecycle and/or tracking and process management tools, e.g., JIRA
  • Good business acumen and experience interpreting data to draw business insights and drive actionable strategies
  • Team oriented, energetic, collaborative, diplomatic, and flexible style
  • Demonstrated intellectual and analytical rigor with strong attention to detail

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.

Oskused

apply blended learning apply for research funding apply research ethics and scientific integrity principles in research activities build recommender systems Business Analytics Business Intelligence collect ICT data communicate with a non-scientific audience Computational Biology Computer Simulation conduct research across disciplines create data models Data Engineering data ethics Data Mining Data Models data quality assessment Data Science data visualisation software define data quality criteria deliver visual presentation of data demonstrate disciplinary expertise design database in the cloud design database scheme develop data processing applications develop professional network with researchers and scientists Digital Curation disseminate results to the scientific community draft scientific or academic papers and technical documentation empirical analysis establish data processes evaluate research activities execute analytical mathematical calculations Hadoop handle data samples Healthcare Analytics image recognition implement data quality processes increase the impact of science on policy and society information categorisation Information Extraction integrate gender dimension in research integrate ICT data interact professionally in research and professional environments interpret current data LDAP LINQ make data-driven decisions manage data manage data collection systems manage findable accessible interoperable and reusable data manage ICT data architecture manage ICT data classification manage intellectual property rights manage open publications manage personal professional development manage research data Marketing Analytics mathematical modelling MDX mentor individuals multidisciplinary research N1QL normalise data online analytical processing operate open source software perform data cleansing perform data mining perform project management perform scientific research promote open innovation in research promote the participation of citizens in scientific and research activities promote the transfer of knowledge publish academic research quantitative analysis query languages report analysis results Research Design resource description framework query language Scientific Computing scientific literature Social Network Analysis SPARQL speak different languages State Estimation statistical modeling techniques Statistics synthesise information teach in academic or vocational contexts think abstractly Unstructured Data use data processing techniques use databases use spreadsheets software visual presentation techniques write scientific publications XQuery

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