Data Science Manager

  • Full-time
  • Job Family Group: Product Development

Company Description

About Visa 

Common Purpose, Uncommon Opportunity. Everyone at Visa works with one goal in mind – making sure that Visa is the best way to pay and be paid, for everyone everywhere. This is our global vision and the common purpose that unites the entire Visa team. As a global payments technology company, tech is at the heart of what we do. CyberSource, a Visa company, has been and continues to be a pioneer within the e-Commerce Payment Management world. Our VisaNet network is capable of handling over 65,000 transaction messages per second for people and businesses around the world, enabling them to use digital currency instead of cash and checks.

We are also global advocates for financial inclusion, working with partners around the world to help those who lack access to financial services join the global economy. Visa’s sponsorships, including the Olympics and FIFA™ World Cup, celebrate teamwork, diversity, and excellence throughout the world. If you have a passion to make a difference in the lives of people around the world, Visa offers an uncommon opportunity to build a strong, thriving career. Visa is fueled by our team of talented employees who continuously raise the bar on delivering the convenience and security of digital currency to people all over the world. Join our team and find out how Visa is everywhere you want to be.

Job Description

The Data Science Manager, Visa Consulting & Analytics (VCA) will be part of a high performing team of consultants and data scientists who engage with senior leaders at Visa’s client organizations through impactful, data-driven consulting & analytics projects.  These engagements help our issuing, acquiring and merchant clients drive the growth and improved profitability of their payments businesses.

In this role, you will be responsible for helping to develop and deliver analytic projects that solve clients' business objectives. You will get a chance to leverage your technical knowledge of big data and data mining techniques and hone your business acumen. Based on findings from data-driven analysis, you will draw fact-based insights and make actionable recommendations that reflect the client's business and context. You will be required to work with large data sets using quantitative techniques and build complex statistical models that learn from big data. Data sets will include, but not limited to, VisaNet transaction data, data from diverse systems maintained by card issuing clients and alternate data sourced from non-traditional data vendors. We have a highly collaborative process and you are required to work across multiple teams and functions for developing cutting edge, creative and advanced analytic solutions and processes. This role is critical in building market-relevant client solutions and intellectual property for Visa.

The role is focused on high growth markets in South East Asia – Indonesia, Philippines, Vietnam, Myanmar and Cambodia, reporting into the geographic Data Science Head, and will be based in Singapore.

Responsibilities 

  • Collaborating with the internal Visa team and external clients to understand the business problem and desired business outcome
  • Defining detailed scope and methodology, creating and executing on the framework with appropriate data mining techniques
  • End-to-end delivery of multiple small to large projects individually, or as part of a project team, within timelines and budget
  • Extract actionable business insights from the data and build deliverables that clearly communicate the findings and recommendations
  • Research industry metrics and business context and bring this context to bear in analyses
  • Actively seek out opportunities to innovate by using non-traditional data and new modeling techniques
  • Ensuring all project documentation is up to date and maintain the highest levels of quality in deliverables
  • Work as independent contributor mostly and as a mentor to other team members by providing guidance when required
  • Enhancing existing analytic techniques by promoting new methodology and best practices in analytics field
  • Oversee deployment and implementation of analytics solutions, and track business value impact
  • Develop strategies and recommendations on testing and monitoring analytical model performance in the business context

Qualifications

Technical 

  • Minimum of 7 years of analytical experience in applying statistical   solutions to business problems 
  • Post Graduate degree (Masters  or Ph.D.) in Quantitative field such as Statistics, Mathematics, Computer  Science, Economics, or equivalent experience preferred 
  • Hands on experience with one or more data analytics/programming tools such as SAS/Salford SPM/Hadoop/R/SQL/Python/Hive 
  • Proficiency in some of the following statistical techniques: Neural Networks, Gradient Boosting, Linear & Logistic Regression, Decision Trees, Random Forests, Markov Chains, Support Vector Machines, Clustering, Principal Component Analysis, Factor analysis, etc 
  • Demonstrated experience in planning, organizing, and managing multiple analytic projects with diverse cross-functional stakeholders
  • Demonstrated ability to innovate solutions to solve business problems

Business

  • Results oriented with strong analytical and problem solving skills, with demonstrated intellectual and analytical rigor 
  • Good business acumen with strong ability to solve business problems through data driven quantitative methodologies. Experience in cards/payments, retail banking, or retail merchant industries is important
  • Team oriented, collaborative, diplomatic, and flexible style, with the ability to tailor data driven results to various audience levels
  • Detailed oriented, is expected to ensure highest level of quality/rigor in reports & data analysis
  • Proven skills in translating   analytics output to actionable recommendations, and delivery
  • Experience in presenting ideas and analysis to stakeholders
  • Exhibit intellectual curiosity and strive to continually learn
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