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

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 an Associate Data Scientist does at Visa:

The Associate 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 an Associate 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

• Directing the execution of medium to small 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.
• Defining detailed analytic scope and methodology, and creating analytic plan
• Executing on the analytic plan with appropriate data mining and analytic techniques
• Performing QA on data and deliverables by analysts and own deliverables
• 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

Qualifications

What you will need:

Technical

• Hands on experience with one or more data analytics or programming tools such as SAS or Python.
• Proficiency in some of the following statistical techniques: Linear & Logistic Regression, Decision Trees, Random Forests, K-Nearest Neighbors, Markov Chain Monte Carlo, Gibbs Sampling etc., Evolutionary Algorithms (e.g., Genetic Algorithms, Genetic Programming), Support Vector Machines, Neural Networks etc.
• Working knowledge in advanced data mining and modeling: Predictive modeling techniques (e.g., binomial and multinomial regression, ANOVA)， Classification techniques (e.g., Clustering, Principal Component Analysis, factor analysis)， Decision Tree techniques (e.g., CART, CHAID)
• 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 payment, retail banking, or retail merchant industries is preferred.
• 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 or 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.

What will also help:

• Post Graduate degree in Quantitative field such as Statistics, Mathematics, Operational Research, Computer Science, Economics, or engineering or equivalent experience preferred.
• Understanding of Cards or Payments and Banking business model would be a plus.
• Proficient in Chinese (Mandarin) language

Projects you will be a part of:

• New Analyst tools and algorithm.
• Beside data analyst, the consulting skill is need to development.

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.