Job Title:

Senior Data Scientist

Overview:

We are the global technology company behind the world’s fastest payments processing network. We are a vehicle for commerce, a connection to financial systems for the previously excluded, a technology innovation lab, and the home of Priceless®. We ensure every employee has the opportunity to be a part of something bigger and to change lives. We believe as our company grows, so should you. We believe in connecting everyone to endless, priceless possibilities.

The Mastercard Launch program is aimed at early career talent, to help you develop skills and gain cross-functional work experience. Over a period of 18 months, Launch participants will be assigned to a business unit, learn and develop skills, and gain valuable on the job experience.

Mastercard has over 2 billion payment cards issued by 25,000+ banks across 190+ countries and territories, amassing over 10 petabytes of data. Millions of transactions are flowing to Mastercard in real-time providing an ideal environment to apply and leverage AI at scale. The AI team is responsible for building and deploying innovative AI solutions for all divisions within Mastercard securing a competitive advantage. Our objectives include achieving operational efficiency, improving customer experience, and ensuring robust value propositions of our core products (Credit, Debit, Prepaid) and services (recommendation engine, anti-money laundering, fraud risk management, cybersecurity)

Role:
• Gather relevant information to define the business problem
• Creative thinker capable of linking AI methodologies to identified business challenges
• Develop AI/ML applications leveraging the latest industry and academic advancements
• Ability to work cross-functionally, and across borders drawing on a broader team of colleagues to effectively execute the AI agenda

All About You:
:• Demonstrated passion for AI competing in sponsored challenges such as Kaggle
• Previous experience with or exposure to:
•Deep Learning algorithm techniques, open source tools and technologies, statistical tools, and programming environments such as Python, R, and SQL
• •Big Data platforms such as Hadoop, Hive, Spark, GPU Clusters for deep learning
•Classical Machine Learning Algorithms like Logistic Regression, Decision trees, Clustering (K-means,
•Hierarchical and Self-organizing Maps), TSNE, PCA, Bayesian models, Time Series ARIMA/ARMA, •Recommender Systems - Collaborative Filtering, FPMC, FISM, Fossil
•Deep Learning algorithm techniques like Random Forest, GBM, KNN, SVM, Bayesian, Text Mining techniques, Multilayer Perceptron, Neural Networks – Feedforward, CNN, LSTM’s GRU’s is a plus. •Optimization techniques – Activity regularization (L1 and L2), Adam, Adagrad, Adadelta concepts; Cost •Functions in Neural Nets – Contrastive Loss, Hinge Loss, Binary Cross entropy, Categorical Cross entropy; developed applications in KRR, NLP, Speech and Image processing
•Deep Learning frameworks for Production Systems like Tensorflow, Keras (for RPD and neural net architecture evaluation), PyTorch and Xgboost, Caffe, and Theono is a plus
• Concentration in Computer Science