Data Scientist Risk Specialist (CEMEA)
- Dubai - United Arab Emirates
As the world's leader in digital payments technology, Visa's mission is to connect the world through the most creative, reliable and secure payment network - enabling individuals, businesses, and economies to thrive. Our advanced global processing network, VisaNet, provides secure and reliable payments around the world, and is capable of handling more than 65,000 transaction messages a second. The company's dedication to innovation drives the rapid growth of connected commerce on any device, and fuels the dream of a cashless future for everyone, everywhere. As the world moves from analog to digital, Visa is applying our brand, products, people, network and scale to reshape the future of commerce.
At Visa, your individuality fits right in. Working here gives you an opportunity to impact the world, invest in your career growth, and be part of an inclusive and diverse workplace. We are a global team of disruptors, trailblazers, innovators and risk-takers who are helping drive economic growth in even the most remote parts of the world, creatively moving the industry forward, and doing meaningful work that brings financial literacy and digital commerce to millions of unbanked and underserved consumers.
You're an Individual. We're the team for you. Together, let's transform the way the world pays.
We are seeking an innovative and analytical thinker to work with our Data Science and Consulting teams in the Central Europe, Middle East and Africa (CEMEA) region. The Data Scientist Risk Director is expected to drive and execute on business development, develop predictive and prescriptive models, context-based prototypes, and high impact storyboards to promote a data-driven strategy and solutions approach for the client (internal and external). The position will primarily focus on engagements in the area of credit, fraud and operational risks, deep risk analytics, risk scoring and ratings as well as forecasting solutions.
- Serve as an analytics expert in designing, developing and implementing best-in-class risk analytic solutions, inclusive of scoring and non-scoring models.
- 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 teaming environment that includes the rest of the Data Science and Consulting team.
- Draft detailed scope for assigned projects, addressing suggested methodology, analytics and development plan.
- Execute on the analytics and development 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 and development 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.
- Provide subject matter expertise and quality assurance of complex data-driven analytic projects.
- Minimum of 7+ years of analytics expertise in applying statistical solutions to business problems.
- Excellent knowledge, experience and understanding of quantitative techniques (modelling, statistics, root-cause, etc.) applied to Risk Management with a focus on Card and Payments. Familiarity with key Risk and Performance Indicators.
- Good understanding of the Payments and Banking Industry including aspects such as consumer credit, consumer debit, prepaid, small business, commercial, co-branded and merchant portfolios.
- Experience working in one or more of the Card and Payments markets around the globe.
- Familiarity in working with big data, both structured and unstructured.
- Proven ability to develop high-quality, production-ready quantitative models for business consumption; machine learning techniques preferred.
- Working knowledge of code optimization best practices for run-time performance.
- Post-graduate degree (Masters or PhD) in a Quantitative field such as Statistics, Mathematics, Operational Research, Computer Science, Economics, Engineering, or equivalent.
- 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.
- 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 solution architecture frameworks that rely on API’s and micro-services.
- Familiarity with common data modeling approaches; ability to work with various datatypes including JSON, XML, etc.
- Familiarity with building data pipelines (e.g. ETL, data preparation, data aggregation and analysis) using tools such as NiFi, Sqoop, Ab Initio; practical experience 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, Gradient Boosting, 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).
- Experience with model governance processes in a highly regulated industry; financial services preferred.
- Deliver results within committed scope, timeline and budget.
- Very strong people/project management skills and experience.
· Ability to travel within CEMEA on short notice.
- Results-oriented with strong problem solving skills and demonstrated intellectual and analytical rigor.
- Good business acumen with a track record in solving business problems through data-driven quantitative methodologies.
- Experience in Cards and Payments, Retail Banking, or Retail Merchant industries preferred.
- Very detailed oriented, is expected to ensure highest level of quality/rigor in reports and data analysis.
- Proven skills in translating analytics output to actionable recommendations and delivery.
- Experience in presenting ideas and analysis to stakeholders whilst tailoring data-driven results to various audience levels.
- Demonstrates integrity, maturity and a constructive approach to business challenges.
- Serves as a role model for the organization and implementing core Visa Values.
- Maintains respect for individuals at all levels in the workplace.
- Strives for excellence and extraordinary results.
- Uses sound insights and judgments to make informed decisions in line with business strategy and needs.
- Able to allocate tasks and resources across multiple lines of business and geographies.
- Demonstrates ability to influence senior management within and outside Data Science groups.
- Can successfully persuade/influence internal stakeholders towards building best-in-class solutions.
- Provides change management leadership.
- Team oriented, collaborative, diplomatic, and flexible style.
- Exhibits intellectual curiosity and a desire for continuous learning.