Lead Data Scientist

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 ScientistJob Description for Lead Data Scientist
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Who is Mastercard?
Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments, and businesses realize their greatest potential.

Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all.
Overview

Finicity, a Mastercard company, is leading the Open Banking Initiative to increase the Financial Health of consumers and businesses. The Data Science and Analytics team is looking for a Data Scientist II. The Data Science team works on Intelligent Decisioning; Financial Certainty; Attribute, Feature, and Entity Resolution; Verification Solutions and much more. Join our team to make an impact across all sectors of the economy by consistently innovating and problem-solving. The ideal candidate is passionate about leveraging data to provide high quality customer solutions. Also, the candidate is a strong technical leader who is extremely motivated, intellectually curious, analytical, and possesses an entrepreneurial mindset.
Role
● Develops machine-learning models to monitor open banking transactions in order to glean insights from the data and create data science algorithms to detect data anomaly observed in fraudulent transactions.
● Manipulates large data sets and applies various technical and statistical analytical techniques (e.g., OLS, multinomial logistic regression, LDA, clustering, segmentation) to draw insights from large datasets.
● Apply various Machine learning (i.e. SVM, Radom Forest, XGBoost, LightGBM, CATBoost etc), Deep learning techniques (i.e. LSTM, RNN, Transformer etc.) to solve analytical problem statement.
● Design and implement machine learning models for a number of financial applications including but not limited to: Transaction Classification, Temporal Analysis, Risk modeling from structured and unstructured data.
● Measure, validate, implement, monitor and improve performance of both internal and external facing machine learning models.
● Propose creative solutions to existing challenges that are new to the company, the financial industry and to data science.
● Present technical problems and findings to business leaders internally and to clients succinctly and clearly.
● Leverage best practices in machine learning and data engineering to develop scalable solutions.
● Identify areas where resources fall short of needs and provide thoughtful and sustainable solutions to benefit the team
● Be a strong, confident, and excellent writer and speaker, able to communicate your analysis, vision and roadmap effectively to a wide variety of stakeholders
● Test current cutting-edge AI technologies to enhance data science modeling work.
All about you:

● 5-7 years in data science/ machine learning model development and deployments
● Exposure to financial transactional structured and unstructured data, transaction classification, risk evaluation and credit risk modeling is a plus.
● A strong understanding of NLP, Statistical Modeling, Visualization and advanced Data Science techniques/methods.
● AI experience is a plus.
● Gain insights from text, including non-language tokens and use the thought process of annotations in text analysis.
● Solve problems that are new to the company, the financial industry and to data science
● SQL / Database experience is preferred.
● Strong Python programming background/experience.
● Experience with Kubernetes, Containers, Docker, REST APIs, Event Streams or other delivery mechanisms.
● Familiarity with relevant technologies (e.g. Tensorflow, Sklearn, Pandas, etc.).
● Familiarity with Databricks Platform.
● Strong desire to collaborate and ability to come up with creative solutions.
● Additional Finance and FinTech experience preferred.
● Bachelor's or Master's Degree in Computer Science, Information Technology, Engineering, Mathematics, Statistics.

Corporate Security Responsibility
Every person working for, or on behalf of, Mastercard is responsible for information security. All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and therefore, it is expected that the successful candidate for this position 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.

NOTE: Candidates go through a thorough screening and interview process.

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.

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