GCC Data Science Manager

Visa

Job Description

Roles & Responsibilities

Desired Candidate Profile

Minimum of 8+ years of expertise in applying Machine Learning solutions to business problems model development and production experience required

Post-graduate degree (Masters or PhD) in a quantitative field such as Statistics, Mathematics, Data Science, Operational Research, Computer Science, Informatics, Economics, or Engineering

Experience working in one or more of the Card & Payments markets around the globe, with specific responsibilities in payments, retail banking, or retail merchant industries

Good understanding of Payments and the Banking industry, including card verticals such as consumer credit, consumer debit, prepaid, small business, commercial and co-branded product

Expert knowledge of data, market intelligence, business intelligence, and AI-driven tools and technologies, with demonstrated ability to incorporate new techniques to solve business problems

Experience planning, organizing, and managing multiple large projects with diverse cross-functional teams, including resource planning and delivery implementation

Experience in presenting ideas and analysis to stakeholders whilst tailoring data-driven results to various audience levels

Proven ability to deliver results within committed scope, timeline and budget

Very strong people/project management skills and experience

Ability to travel within GCC on short notice

Technical Expertise:

• Expertise in distributed computing environments / big data platforms (Hadoop, Elasticsearch, etc.) as well as common database systems and value stores (SQL, Hive, HBase, etc.)
• Familiarity with workings of Large Language models and ability to develop agents
• Capability to develop front end UI/UX would be plus
• Strong programming ability in different programming languages such as Python, Spark, 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)