Middle Data Scientist

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

We are currently seeking for individual contributor to CIS&SEE Data Science team to support our clients. The key responsibilities of the role will include descriptive, as well as predictive and prescriptive analytical projects and limited number of regular and ad-hoc reporting activities. This role assumes support of internal clients in the first place, with some exposure to external clients’ projects.

As a manager you will be accountable for leading the design, development and implementation of analytics driven strategies and solutions. In this role you will partner with Visa Consulting & Analytics, Business, Finance and Technology teams across the organization to implement solutions to drive business performance.

Data Science team within Visa

Data Science is responsible for blueprinting and delivering projects with the appropriate analytic methodologies and techniques to solve client’s business objectives. The team closely collaborates with other analytic 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 big data 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.

Global Data Science team is the engine of analytics at Visa, this is a high-performing team of data scientists, data analysts, statisticians, and business analysts from a variety of countries – serving the Asia Pacific, Central Europe, Middle East and Africa geographies.
Visa DS is looking for a hands-on manager, a person that earns trust and respect of the team. The Manager must be results oriented, highly organized, and must, must be focused on delivering innovative analytics work.

Qualifications

• Experience with modeling software, experience with Python, Hadoop, Hive, Impala or similar instruments, Practical experience in building and applying machine learning models (regression, clustering, classification: gradient boosting, random forests, linear models, deep learning etc.), understanding in how these algorithms work and end-to-end development skills from business understanding and data preparation to quality assurance of ML models
• Minimum of 1 year of analytical expertise in applying statistical solutions to business problems (experience in payments and/or consumer banking and/or commercial banking and/or FMCG retail and/or consulting and/or heavy industry
• Defining and designing analytic approaches to business problems, decomposing heavy business problems into structured and time predictable analytical tasks
• Excellent communication skills in both spoken and written English (upper intermediate plus), Ukrainian fluent speaker

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.