Team Summary

Data Science team is a key part of decision management engine at Visa; this is a high-performing team of data scientists, data analysts, statisticians, and business analysts from a variety of countries – serving major FIs and merchants in Asia Pacific. Analytics & Data Products is looking for a hands-on person that earns trust and respect of the team. The person must be results oriented, highly organized, and must, must be focused on delivering innovative analytics work.

What does Associate Data Scientist Does

The Data Scientist will be working together with other members in the team to design and deliver projects with the appropriate analytic methodologies and techniques to solve client’s business objectives. The team closely collaborates with business stakeholders to understand the business problem in order to determine the most appropriate analytic approach that provides meaningful results to clients. Responsibilities include delivering projects on time and within scope with an in-depth knowledge of data analytics and cutting-edge data mining techniques as well as the use of predictive, classification and alternate analytic algorithms for modeling and segmentation. These analyses are foundational to corroborate or refute stated hypotheses and are incorporated in the final client-facing solutions. The team is responsible for continuously creating and protecting analytic IP resulting from project learning.

As a Data Scientist you will be responsible for developing Data Analytics solutions to solve business problems. Here, you will be required to work with large data sets using quantitative techniques and build complex statistical models that learn from big data. 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.

Why this is important to Visa

The Data Scientist is a key member of the Consulting and Analytics team.  DS is key driver to Visa business.

Responsibilities

• Participating analytic projects along with rest of the stakeholders in both internal and external

• Collaborating with the different teams to fully understand business requirements and desired business outcomes

• Executing on the analytic plan with appropriate data mining and analytic techniques

• Ensuring all project documentation is up to date and all projects are reviewed per analytic plan

• Ensuring project delivery within timelines and budget requirements

• Enhancing existing analytic techniques by promoting new methodology and best practices in analytics field

• Providing subject matter expertise and quality assurance of complex data driven analytic projects

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.

Technical
• Hands on experience with one or more data analytics/programming tools such as Python/SQL
• Proficiency in some of the following statistical techniques: Linear & Logistic Regression, Decision Trees, Random Forests, K-Nearest Neighbors,
• 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
• 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

What will also help:

• Proficient in Chinese (Mandarin) language
• Working location: Beijing

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.