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Visa // credit cards
 
Engineering, Full Time       Posted: Friday, November 08, 2019
 
   
 
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JOB DETAILS
  Company Description

As the world's leader in digital payments technology, Visa's mission is to connect the world through the most creative, reliable & secure payment network - enabling individuals, businesses, & economies to thrive. Our advanced global processing network, VisaNet, provides secure & reliable payments around the world, & 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, & 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 & 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, & be part of an inclusive & diverse workplace. We are a global team of disruptors, trailblazers, innovators & risk-takers who are helping drive economic growth in even the most remote parts of the world, creatively moving the industry forward, & doing meaningful work that brings financial literacy & digital commerce to millions of unbanked & underserved consumers.

You're an Individual. We're the team for you. Together, let's transform the way the world pays.

Job Description

We are seeking an innovative & analytical thinker to work with our Data Science & Consulting teams in the Central Europe, Middle East & Africa (CEMEA) region. The Data Scientist Risk Director is expected to drive & execute on business development, develop predictive & prescriptive models, context-based prototypes, & high impact storyboards to promote a data-driven strategy & solutions approach for the client (internal & external). The position will primarily focus on engagements in the area of credit, fraud & operational risks, deep risk analytics, risk scoring & ratings as well as forecasting solutions.

Principal Responsibilities

  1. Serve as an analytics expert in designing, developing & implementing best-in-class risk analytic solutions, inclusive of scoring & non-scoring models.
  2. Create & deliver powerful insights from data through better visualization & storyboarding.
  3. Collaborate with internal & external partners to fully understand business requirements & desired business outcomes.
  4. Demonstrate execution proficiency in handing multiple medium-to-large analytics projects in a teaming environment that includes the rest of the Data Science & Consulting team.
  5. Draft detailed scope for assigned projects, addressing suggested methodology, analytics & development plan.
  6. Execute on the analytics & development plan with appropriate data mining & analytical techniques.
  7. Perform quality assurance of data & deliverables for work performed by other Data Scientists & self.
  8. Ensure all project documentation is up to date & all projects are reviewed per analytics & development plan.
  9. Ensure project delivery within timelines & budget requirements.
  10. Build on team's analytical skills & business knowledge.
  11. Enhance existing analytics techniques by promoting new methodologies & best practices in the Data Science field.
  12. Provide subject matter expertise & quality assurance of complex data-driven analytic projects.
Qualifications
  • Minimum of 7+ years of analytics expertise in applying statistical solutions to business problems.
  • Excellent knowledge, experience & understanding of quantitative techniques (modelling, statistics, root-cause, etc.) applied to Risk Management with a focus on Card & Payments. Familiarity with key Risk & Performance Indicators.
  • Good understanding of the Payments & Banking Industry including aspects such as consumer credit, consumer debit, prepaid, small business, commercial, co-branded & merchant portfolios.
  • Experience working in one or more of the Card & Payments markets around the globe.
  • Familiarity in working with big data, both structured & 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, & AI-driven tools & technologies.
  • Experience planning, organizing, & managing multiple large projects with diverse cross-functional teams.
  • Demonstrated ability to incorporate new techniques to solve business problems.
  • Demonstrated resource planning & delivery skills.

Technical Expertise

  • Experience in distributed computing environments / big data platforms (Hadoop, Elasticsearch, etc.) as well as common database systems & value stores (SQL, Hive, HBase, etc.).
  • Ability to write scratch MapReduce jobs & fluency with Spark frameworks.
  • Familiarity with both common computing environments (e.g. Linux, Shell Scripting) & commonly-used IDE's (Jupyter Notebooks); proficiency in SAS technologies & techniques.
  • Strong programming ability in different programming languages such as Python, R, Scala, Java, Matlab, C++, & SQL.
  • Experience in solution architecture frameworks that rely on API's & 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 & analysis) using tools such as NiFi, Sqoop, Ab Initio; practical experience with data lineage processes & 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 & statistical modeling techniques, including Predictive Modeling (e.g., binomial & 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 & budget.
  • Very strong people/project management skills & experience.

Ability to travel within CEMEA on short notice.

Business Experience

  • Results-oriented with strong problem solving skills & demonstrated intellectual & analytical rigor.
  • Good business acumen with a track record in solving business problems through data-driven quantitative methodologies.
  • Experience in Cards & Payments, Retail Banking, or Retail Merchant industries preferred.
  • Very detailed oriented, is expected to ensure highest level of quality/rigor in reports & data analysis.
  • Proven skills in translating analytics output to actionable recommendations & delivery.
  • Experience in presenting ideas & analysis to stakeholders whilst tailoring data-driven results to various audience levels.

Leadership Competencies

  • Demonstrates integrity, maturity & a constructive approach to business challenges.
  • Serves as a role model for the organization & implementing core Visa Values.
  • Maintains respect for individuals at all levels in the workplace.
  • Strives for excellence & extraordinary results.
  • Uses sound insights & judgments to make informed decisions in line with business strategy & needs.
  • Able to allocate tasks & resources across multiple lines of business & geographies.
  • Demonstrates ability to influence senior management within & outside Data Science groups.
  • Can successfully persuade/influence internal stakeholders towards building best-in-class solutions.
  • Provides change management leadership.
  • Team oriented, collaborative, diplomatic, & flexible style.
  • Exhibits intellectual curiosity & a desire for continuous learning.
 
 
 
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