Senior Data Scientist (Python)

Basic Qualifications:

Preferred Qualifications:

• 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
• Experience in distributed computing environments , big data platforms (Hadoop, Elasticsearch, etc) as we'll 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 (eg 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 (eg ETL, data preparation, data aggregation and analysis) using tools such as NiFi, Sqoop, Ab Initio familiarity with data lineage processes and schema management tools.
• 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 (eg Genetic Algorithms, Genetic Programming), Support Vector Machines, Neural Networks, etc
• Expert knowledge of advanced data mining and statistical modeling techniques, including Predictive modeling (eg, binomial and multinomial regression, ANOVA) Classification techniques (eg, Clustering, Principal Component Analysis, factor analysis) Decision Tree techniques (eg, CART, CHAID)
• Deliver results within committed scope, timeline and budget
• Very strong people/project management skills and experience.