Principal Data Scientist

MasterCard is a technology company in the global payments business.

We connect consumers, financial institutions, merchants, governments and businesses worldwide and enable them to use secure and convenient electronic forms of payment.

Overview.

The Cyber and Intelligence Platform Data Science team is responsible for creating deep learning Artificial Intelligence (A.I.) and Machine Learning (M.L.) models.

The models generated are production ready and created to back specific products in Mastercards authentication and authorization networks.

The Data Science team is also responsible for developing automated processes for creating models covering all modeling steps, from data extraction up to delivery.

In addition, the processes must be designed to scale, to be repeatable, resilient, and industrialized.

You will be joining a team of Data Scientists working on innovative A.I.

and M.L.

fraud detection and anti-money laundering solutions.

Our innovative cross-channel AI solutions are applied in Fortune 500 companies in industries such as fin-tech, investment banking, biotech, healthcare, and insurance.

We are pursuing a highly motivated individual with strong problem-solving skills to take on the challenge of structuring and engineering data and cutting-edge A.I.

model evaluation and reporting processes.

Role.

As a Principle Data Scientist, you will:.

Work closely with the business owners to understand business requirements, performance metrics regarding data quality and model performance of customer facing products.

Work with multiple disparate sources of data, storage systems, and build processes and pipelines to provide cohesive datasets for analysis and modeling.

Generate and maintain and optimize data pipelines for model building and model performance evaluation.

Overall responsibility for development, testing, and evaluation of modern machine learning and A.I.

models for specific products.

Oversee implementation of models.

Evaluate production models based on business metrics to drive continuous improvement.

All About You.

Essential Skills:.

Data engineering experience.

Experience with SQL language and one or multiple of the following database technologies: PostgreSQL, Hadoop, Netezza, Spark, Oracle.

Good knowledge of Linux / Bash environment.

Python and one of the following machine learning libraries.

o Spark ML.

o TensorFlow or related deep learning frameworks.

o Scikit Learn.

o XGBoost.

Good communication skills.

Highly skilled problem solver.

Exhibits a high degree of initiative.

At least an undergraduate degree in CS, or a STEM related field.

Prior experience in payment fraud detection modeling.

Nice to have:.

Masters or PhD in CS, Data Science, Machine Learning, AI or a related STEM field.

Experience in with data engineering and model building in PySpark using Spark ML on petabyte scale data.

Understands and implements methods to evaluate own work and others for bias, inaccuracy, and error.

Loves working with error-prone, messy, disparate, unstructured data.