• Ensure all AI solution development is in line with industry standards for data management and privacy compliance including the collection, use, storage, access, retention, output, reporting, and quality of data at Mastercard
• Adopt a pragmatic approach to AI, capable of articulating complex technical requirements in a manner this is simple and relevant to stakeholder use cases
• Gather relevant information to define the business problem interfacing with global stakeholders
• Creative thinker capable of linking AI methodologies to identified business challenges
• Identify commonalities amongst use cases enabling a microservice approach to scaling AI at Mastercard, building reusable, multi-purpose models
• Develop AI/ML solutions/applications leveraging the latest industry and academic advancements
• Leverage open and closed source technologies to solve business problems
• Ability to work cross-functionally, and across borders drawing on a broader team of colleagues to effectively execute the AI agenda
• Partner with technical teams to implement developed solutions/applications in production environment
• Support a learning culture continuously advancing AI capabilities
• Experience in the Data Sciences field with a focus on AI strategy and execution and developing solutions from scratch
• Demonstrated passion for AI competing in sponsored challenges such as Kaggle
• Previous experience with or exposure to:
o Deep Learning algorithm techniques, open source tools and technologies, statistical tools, and programming environments such as Python, R, and SQL
o Big Data platforms such as Hadoop, Hive, Spark, GPU Clusters for deep learning
o 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
o 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
o 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
• Exposure or experience using collaboration tools such as:
o Confluence (Documentation)o Bitbucket/Stash (Code Sharing)
o Shared Folders (File Sharing)
o ALM (Project Management)• Knowledge of payments industry a plus
• Experience with SAFe (Scaled Agile Framework) process is a plus
• Effective at managing and validating assumptions with key stakeholders in compressed timeframes, without hampering development momentum
• Capable of navigating a complex organization in a relentless pursuit of answers and clarity
• Enthusiasm for Data Sciences embracing the creative application of AI techniques to improve an organization's effectiveness
• Ability to understand technical system architecture and overarching function along with interdependency elements, as well as anticipate challenges for immediate remediation
• Ability to unpack complex problems into addressable segments and evaluate AI methods most applicable to addressing the segment
• Incredible attention to detail and focus instilling confidence without qualification in developed solutions
• Strong written and oral communication skills
• Strong project management skills
• Concentration in Computer Science
• Some international travel required
