Mastercard

2 weeks ago

Lead Data Scientist

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 9 hours agoStatus: Live

Job Summary

Lead the development of machine learning models for Mastercard Identity Verification, working with large, high-volume transactional data. Guide statistical methods and algorithm decisions, prototype and productionize models, and evaluate/benchmark performance with monitoring. Partner with business owners and product managers to align models to data quality and business metrics, maintain ML pipelines and infrastructure, and oversee model implementation while mentoring the team.

Location: Budapest

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Analyze complex, high-volume data from varying sources to identify patterns, trends, and regularities.
•Prototype and develop machine learning models with an agile team, from concepts to live product integrations.
•Evaluate and benchmark model performance, implement monitoring, and compare results across approaches.
•Maintain model development pipelines, libraries, and ML infrastructure to ensure modern models are well tested.
•Work closely with business owners and product managers on requirements and performance metrics, and oversee model implementation; mentor team members in statistical modeling approaches.

Key Requirements

•Have studied a quantitative field (e.g., mathematics, statistics, economics, data science) at a doctoral or master’s level.
•Strong foundation in statistical learning methods such as linear regression, support vector machines, tree-based methods, neural networks, bagging, and boosting.
•Demonstrated problem-solving and critical thinking to frame problems, challenge assumptions, and validate modeling solutions.
•Strong coding and data skills: write complex queries and use tools such as scikit-learn for Python; produce production-quality code in Python (preferred), R, Java, and/or C++.
•Experience building and deploying ML models for real business problems, including working with large datasets and distributed processing (e.g., Apache Spark), plus cloud services (e.g., AWS/Azure/Google Cloud).

Experience:Machine learningData scienceDistributed data processing

Education:PhD / Doctorate in Mathematics, statistics, economics, or data science

Skills:Critical thinkingProblem solvingCommunicationMentorship

Tech Stack:PythonScikit-learnRJavaC++AWSMicrosoft AzureGoogle CloudTensorflowKerasApache Spark

Company Brief

Mastercard

Mastercard is a global payments and technology company that processes transactions, provides fraud prevention and data services, and builds payment infrastructure for consumers, businesses, merchants and governments across 200+ countries.

Industry: Payments

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Purchase, New York, United States

Founded: 1966

Glassdoor: 4.1

Website LinkedIn Glassdoor

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Mastercard

2 weeks ago

Lead Data Scientist

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 9 hours agoStatus: Live

Job Summary

Location: Budapest

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Analyze complex, high-volume data from varying sources to identify patterns, trends, and regularities.
•Prototype and develop machine learning models with an agile team, from concepts to live product integrations.
•Evaluate and benchmark model performance, implement monitoring, and compare results across approaches.
•Maintain model development pipelines, libraries, and ML infrastructure to ensure modern models are well tested.
•Work closely with business owners and product managers on requirements and performance metrics, and oversee model implementation; mentor team members in statistical modeling approaches.

Key Requirements

•Have studied a quantitative field (e.g., mathematics, statistics, economics, data science) at a doctoral or master’s level.
•Strong foundation in statistical learning methods such as linear regression, support vector machines, tree-based methods, neural networks, bagging, and boosting.
•Demonstrated problem-solving and critical thinking to frame problems, challenge assumptions, and validate modeling solutions.
•Strong coding and data skills: write complex queries and use tools such as scikit-learn for Python; produce production-quality code in Python (preferred), R, Java, and/or C++.
•Experience building and deploying ML models for real business problems, including working with large datasets and distributed processing (e.g., Apache Spark), plus cloud services (e.g., AWS/Azure/Google Cloud).

Experience:Machine learningData scienceDistributed data processing

Education:PhD / Doctorate in Mathematics, statistics, economics, or data science

Skills:Critical thinkingProblem solvingCommunicationMentorship

Tech Stack:PythonScikit-learnRJavaC++AWSMicrosoft AzureGoogle CloudTensorflowKerasApache Spark

Company Brief

Mastercard

Industry: Payments

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Purchase, New York, United States

Founded: 1966

Glassdoor: 4.1

Website LinkedIn Glassdoor