Senior Data Scientist

Team Summary:

Visa Consulting & Analytics (VCA) drives tangible, impactful and financial results for Visa's clients, including card issuers, acquirers, and merchants. Drawing on our expertise in strategy consulting, data analytics, brand management, marketing, operations and macroeconomics, VCA solves the most strategic problems for our clients.

The VCA team within Japan provides a comprehensive range of consulting services and solutions to our clients in Japan, that address unique challenges in areas such as strategic growth, profitability, risk management and digital strategy.

VCA Managed Services members play a key role in enhancing the partnership with one of Visa Japan’s largest clients by supporting strategic planning and implementation of their most strategic initiatives.

What a Senior Data Scientist does at Visa:

• Executing multiple medium to large analytic projects along with rest of the stakeholders in analytics & consulting teams
• Establishing best practices for change management, model automation and production rollout
• Familiarity with batch processing and process monitoring for model refresh
• Meeting with clients to explain model results, answer questions and propose further analysis
• Collaborating closely with clients to analyze clients’ data, extract insights, and create dashboards

Why this is important to Visa

VCA’s services allow our clients to boost their business performance through leveraging Visa’s expansive market insights and data. In addition, Managed Services team collaborates with clients closely in leveraging clients’ data and improving their business with data science capabilities. Our role in Japan in particular is pivotal in advancing the transition towards a more cashless payments landscape.

Responsbilities

• Actively seek out opportunities to improve processes and technologies
• Collaborating with the internal teams to fully understand business requirements and desired business outcomes
• Defining detailed analytic scope and methodology, and creating an analytic plan
• Executing with appropriate statistical and machine learning techniques
• Providing thought leadership in both using data to solve business problems and arriving at innovative technology and statistical solutions
• Ensuring project delivery within timelines and budget requirements
• Work as independent contributor mostly and as a team lead if needed by providing guidance to other team members when required

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.

Requirements

Basic Qualifications
•5 or more years of relevant work experience with a Bachelor's Degree or at
least 2 years of work experience with an Advanced degree (e.g. Masters, MBA,
JD, MD) or 0 years of work experience with a PhD

Preferred Qualifications
•5 years of analytical experience in applying statistical solutions to business
problems
•Graduate degree (Masters or Ph.D.) in computer science, or a quantitative field
such as physics, statistics or mathematics, or equivalent experience
•Proficiency in processing large data sets using Hive and related tools
•Fluency in multiple technologies and languages (Java/C/C++/Python/R/SQL),
with experience in at least one compiled language
•Experience with a wide cross section of machine learning techniques :
clustering, dimensionality reduction, variable selection, cross validation, neural
networks, gradient boosting, linear & logistic regression
•Unix/Linux proficiency : command line tools, shell scripting
•Experience in collaborating with business functions at least internally or
externally
•Native level Japanese and business level English proficiency ( or vice versa )
•Ability to quickly ideate novel mathematical models to address new problems
•Experience in cards/payments, retail banking, or retail merchant industries
•Attrition and propensity modelling
•Recommendation systems and clustering algorithms
•Network analysis
•LTV and forecasting algorithms