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Tier-1 brand, mid-level data scientist title, metro location, and broad ML skillset increase applicant competition.
Core ML skills are transferable, but payments/fraud domain specialization increases sector-specific fit sensitivity.
Extensive mandatory ML, production and big-data technology requirements imply strict technical screening filters.
Define business problems and develop AI/ML applications to address them within Mastercard’s payment ecosystem.
Leverage deep learning, classical machine learning, and big data technologies to drive operational efficiency and improve customer experience.
Collaborate cross-functionally across global teams to deploy AI solutions ensuring a competitive advantage in products like Credit, Debit, Prepaid, and services such as fraud risk management and cybersecurity.
Experience required: Not explicitly mentioned in the JD.
Proficiency in AI/ML technologies including Python, R, SQL, and classical and deep learning algorithms (e.g., Logistic Regression, Random Forest, CNN, LSTM).
Familiarity with big data platforms like Hadoop, Hive, Spark, GPU clusters for deep learning.
Concentration in Computer Science; adherence to Mastercard’s information security policies.
Demonstrated ability to apply advanced AI techniques in production environments using frameworks like TensorFlow, Keras, PyTorch, Xgboost.
Experience or proven interest in competitive AI scenarios (e.g., Kaggle competitions).
Effective cross-border and cross-functional collaboration to translate complex AI methodologies into business impact.