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Lead Data Scientist
2 months ago
Our Purpose
We work 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. We cultivate a
culture of inclusion
for all employees that respects their individual strengths, views, and experiences. We believe that our differences enable us to be a better team - one that makes better decisions, drives innovation and delivers better business results.
Title and Summary
Lead Data Scientist - Generative AI
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.
Services within Mastercard is responsible for acquiring, engaging, and retaining customers by managing fraud and risk, enhancing cybersecurity, and improving the digital payments experience. We provide value-added services and leverage expertise, data-driven insights, and execution.
Overview
Our Data Science team is responsible for creating deep learning Artificial Intelligence (A.I.) and Machine Learning (M.L.) models. The models generated are production ready and created to back specific products in Mastercard’s authentication and authorization networks. The Data Science team is also responsible for developing automated processes for creating models covering all modeling steps, from data extraction up to delivery. In addition, the processes must be designed to scale, to be repeatable, resilient, and industrialized.
As a Lead Data Scientist within the Security Solutions Data Science team, you will pilot groundbreaking Generative AI use cases aimed at revolutionizing sales enablement. By automating qualitative research and leveraging Mastercard’s network intelligence, your work will help us drive targeted, data-driven sales strategies that resonate with diverse industries and client needs.
The Role:
We are looking for a Lead Data Scientist with deep expertise in generative AI to spearhead projects that redefine how businesses approach automation, content creation, and predictive modeling. As a Lead Data Scientist within the Security Solutions Data Science team, you will pilot groundbreaking Generative AI use cases aimed at revolutionizing sales enablement. By automating qualitative research and leveraging Mastercard’s network intelligence, your work will help us drive targeted, data-driven sales strategies that resonate with diverse industries and client needs.
Key Responsibilities:
- Collaborate with cross-functional teams (data scientists, engineers, product managers, and sales) to design and develop innovative AI models that solve real-world problems.
- Build and optimize data pipelines for model creation, testing, and evaluation.
- Work with diverse datasets and deploy cutting-edge machine learning techniques to improve business outcomes.
- Share your insights by delivering best practices, tutorials, and tailored content to technical and non-technical stakeholders alike
- Lead the design, development, and deployment of generative AI models, including natural language processing (NLP), deep learning (DL), and reinforcement learning (RL) approaches.
- Build and optimize large-scale machine learning pipelines to process vast datasets and enhance model performance.
- Communicate complex technical concepts to both technical and non-technical stakeholders, providing insights and recommendations to drive business outcomes.
- Ensure the scalability and reliability of AI models in production environments.
All About You:
- Proven experience as a data scientist, especially in building large-scale machine learning or deep learning models (e.g., CNN, RNN).
- Strong expertise in Python and extensive experience building large scale machine learning models with LLM using technologies such as TensorFlow, PyTorch, MXNet or Keras.
- Knowledge of data pipeline tools like Apache NiFi, Airflow, or equivalents.
- Experience working with SQL, PostGreSQL and familiarity of working with large datasets and distributed computing frameworks (e.g., Hadoop, Spark,).
- Expertise in natural language processing (NLP), deep learning (DL), and other AI subfields.
- Familiarity with cloud
