**Our Purpose** *Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.* **Title and Summary** ### Lead Data Scientist ### Overview Services within Mastercard is responsible for acquiring, engaging, and retaining customers by managing fraud and risk, enhancing cybersecurity, and improving the digital experience. We provide value-added services and leverage expertise, data-driven insights, and execution. We are a division within Mastercard that specializes in Identity Verification, providing businesses worldwide the ability to link any digital transaction to the human behind it. Our Identity Engine, the first and only of its kind, uses complex machine learning to combine features derived from the billions of transactions within our proprietary network and the data from our graph to deliver industry leading risk assessment solutions. Role We are looking for a Lead Data Scientist in a related field to join our team in the Budapest office. The position will be technical in nature where you will guide and drive decisions on various statistical methods and algorithms used in modeling work of our team. You will also provide mentorship to other team members in this area. This is a key role within the team responsible for developing models to support various Mastercard Identity Verification products, and you’ll have exciting responsibilities, including: • Analyzing complex, high-volume data from varying sources and identifying key regularities, patterns and trends. • Prototyping and developing machine learning models in collaboration with an agile, high-functioning team. • Spotting new opportunities in data collection, feature creation, feature selection, model tuning and evaluation practices, and taking those ideas from the first concepts to live product integrations. • Evaluating and benchmarking for model performance comparison. Implementing effective monitoring. • Leveraging new research in data modelling to identify opportunities, pioneering algorithms and systems that become key commercial products. • Maintaining model development pipelines, libraries and machine learning infrastructure. Ensuring modern machine learning models are well tested. • Working closely with business owners and product managers to understand business requirements, performance metrics regarding data quality and model performance of our new products. • Overseeing implementation of models. All About You Ideally, you are: • Statistically adept. You have studied in a quantitative field (i.e. mathematics, statistics, economics, data science) at a doctoral or master’s level. You have the depth of knowledge required to identify appropriate techniques, follow and create formal proofs, define apt performance measures and adeptly explore or transform data. • Someone with a strong foundational knowledge of principles underlying common statistical learning techniques such as linear regression, support vector machine, tree-based methods, neural networks, bagging and boosting methods. • A strong problem solver with critical thinking skills who can formulate a problem into solution. Able to challenge assumptions and validate modeling solutions from a statistical inference perspective. • Capable of writing complex queries to process data. Proficient with manipulating and analyzing data to gain meaningful insights using tools such as scikit-learn for Python. • Experienced in creating algorithms and applying machine learning models to solve real business problems. You have the vision to see what the next generation model looks like and can iterate over production models to generate a competitive edge in the market. • A capable coder, able to write well-abstracted, production-quality code in Python (preferred), R, Java and/or C++. You’re experienced in using cloud services (e.g. AWS, Microsoft Azure and/or Google Cloud), and machine learning tools (e.g. scikit-learn, Tensorflow and/or Keras). • Experienced working with large data sets. You understand the benefits of batch processing and parallelization and know how to design a pipeline to scale-out machine learning workflows. You have experience working with distributed data processing frameworks such as Apache Spark. • An effective communicator in visual, verbal and spoken channels, able to identify a narrative in complex data and convey clear, actionable findings to different types of audiences. • Experienced in architecting end-to-end solutions for production deployment. ### ### **Corporate Security Responsibility** All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must: - Abide by Mastercard’s security policies and practices; - Ensure the confidentiality and integrity of the information being accessed; - Report any suspected information security violation or breach, and - Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.