Senior Data Scientist & ML Platform Engineer

Mastercard; New York (NY); On-site; USD 138,000 - 221,000; Full time

Benefits offered by this job

Health insurance

Flexible Spending Account

Paid time off

Job summary

Mastercard is seeking a Senior Data Scientist to build scalable data platforms, align data science with business needs, and deliver production-ready ML solutions. You’ll work in the Loyalty group, leveraging real-time processing, cloud infrastructure, and an agile, collaborative team culture.

You will design end-to-end data pipelines, deploy models to production, and partner with product and engineering to translate business requirements into technical solutions.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or related field.
• Multiple years of professional experience in data science and/or data engineering.
• Strong programming skills in SQL and Python.
• Hands-on experience with ML frameworks (scikit-learn, TensorFlow, PyTorch).
• Experience building data pipelines with Airflow and Spark.
• Solid understanding of statistics and probability.

Responsibilities

• Develop, validate, and deploy ML models for business use cases.
• Perform exploratory data analysis to uncover trends and insights.
• Apply statistical techniques to solve business problems.
• Communicate findings with visualizations and reports.
• Design and run A/B tests and experiments.
• Build and maintain scalable data pipelines (ETL/ELT).
• Work with large datasets in distributed environments.
• Collaborate with product, engineering, and business teams.

Skills

Machine Learning & Statistics

Data Engineering

SQL & Data Manipulation

Cloud & Big Data

Problem Solving

Communication

Education

Bachelor’s or Master’s in CS/DS/Statistics

Tools

Airflow

Spark

TensorFlow

PyTorch

Job description

Mastercard is seeking a Senior Data Scientist to build scalable data platforms, align data science with business needs, and deliver production-ready ML solutions. You’ll work in the Loyalty group, leveraging real-time processing, cloud infrastructure, and an agile, collaborative team culture.

You will design end-to-end data pipelines, deploy models to production, and partner with product and engineering to translate business requirements into technical solutions.