Data Scientist

Company Description

Visa is a world leader in digital payments, facilitating more than 215 billion payments transactions between consumers, merchants, financial institutions and government entities across 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.

When you join Visa, you join a culture of purpose and belonging - where your growth is priority, your identity is embraced, and the work you do matters. We believe that economies that include everyone everywhere, uplift everyone everywhere. Your work will have a direct impact on billions of people around the world - helping unlock financial access to enable the future of money movement.

**Join Visa: A Network Working for Everyone.**

**Job Description**:
We are seeking an innovative and analytical thinker to support our Data Science team in the Middle East and North Africa (MENA) region based in Cairo, Egypt. As a Data Scientist, you are expected to generate data and business insights, develop predictive and prescriptive models, context-based prototypes, and high impact storyboards to promote a data-driven strategy and solutions approach for the company.
- Serve as an analytics expert in designing, developing and implementing best in analytic solutions
- Create and deliver powerful insights from data through better visualization and storyboarding
- Collaborate with internal and external partners to fully understand business requirements and desired business outcomes
- Demonstrate execution proficiency in handing multiple medium-to-large analytics projects in a team environment that includes the rest of the Data Science team
- Draft detailed scope for assigned projects, addressing suggested methodology and analytics plan
- Execute on the analytics plan with appropriate data mining and analytical techniques
- Perform quality assurance of data and deliverables for work performed by other Data Scientists and self
- Ensure all project documentation is up to date and all projects are reviewed per analytics plan
- Ensure project delivery within timelines and budget requirements
- Build on team’s analytical skills and business knowledge
- Enhance existing analytics techniques by promoting new methodologies and best practices in the Data Science field

**Qualifications**:
**Professional Experience**
- Experience working in one or more of the Card Payments markets around the globe
- Post-graduate degree (Masters or PhD) in a Quantitative field such as Statistics, Mathematics, Operational Research, Computer Science, Economics, Engineering, or equivalent
- Good understanding of the Payments and Banking Industry including aspects such as consumer credit, consumer debit, prepaid, small business, commercial, co-branded and merchant
- Good knowledge of data, market intelligence, business intelligence, and AI-driven tools and technologies
- Experience planning, organizing, and managing multiple large projects with diverse cross-functional teams
- Demonstrated ability to incorporate new techniques to solve business problems
- Demonstrated resource planning and delivery skills

**Technical Expertise**
- Experience in distributed computing environments / big data platforms (Hadoop, Elasticsearch, etc.) as well as common database systems and value stores (SQL, Hive, HBase, etc.)
- Ability to write scratch MapReduce jobs and fluency with Spark frameworks
- Familiarity with both common computing environments (e.g. Linux, Shell Scripting) and commonly-used IDE’s (Jupyter Notebooks); proficiency in SAS technologies and techniques
- Strong programming ability in different programming languages such as Python, R, Scala, Java, Matlab, C++, and SQL
- Experience in drafting solution architecture frameworks that rely on API’s and micro-services
- Familiarity with common data modeling approaches, and ability to work with various datatypes including JSON, XML, etc.
- Ability to build data pipelines (e.g. ETL, data preparation, data aggregation and analysis) using tools such as NiFi, Sqoop, Ab Initio; familiarity with data lineage processes and schema management tools such as Avro
- Proficient in some or all of the following techniques: Linear & Logistic Regression, Decision Trees, Random Forests, K-Nearest Neighbors, Markov Chain, Monte Carlo, Gibbs Sampling, Evolutionary Algorithms (e.g. Genetic Algorithms, Genetic Programming), Support Vector Machines, Neural Networks, etc.
- Expert knowledge of advanced data mining and statistical modeling techniques, including Predictive modeling (e.g., binomial and multinomial regression, ANOVA); Classification techniques (e.g., Clustering, Principal Component Analysis, factor analysis); Decision Tree techniques (e.g., CART, CHAID)
- Deliver results within committed scope, timeline and budget
- Very strong people/project management skills and experience
- Ability to trave