Senior Data Scientist, AI Engineering

Senior Data Scientist, AI Engineering at Mastercard scores 98 out of 100 on AI centrality, which makes it a Level 4 role on this board.

Dublin, IrelandseniorFull time

AI in this role

pytorchtensorflowscikit-learnxgboostdatabricks

rag

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

Senior Data Scientist, AI Engineering

Overview

Mastercard's AI Centre of Excellence is building the next generation of AI capabilities powered by large-scale transaction data, machine learning, and foundation models. We are transforming how AI solutions are developed by enabling teams to leverage reusable learned intelligence rather than building bespoke feature-engineering pipelines for every use case.

We are seeking a Senior Data Scientist, AI Engineering to develop advanced machine learning solutions across domains. This role combines deep expertise in predictive modelling, experimentation, and applied machine learning to deliver measurable business impact. The successful candidate will partner closely with engineering, product, and business teams to bring innovative AI solutions from concept to production.

What You'll Work On

This role focuses on applying machine learning, predictive modelling, and foundation-model representations to solve business problems at scale. Typical use cases include forecasting, propensity modelling, recommendation systems, behavioural analytics, and customer intelligence.

While familiarity with Generative AI is beneficial, this is primarily an applied machine learning and data science role rather than a conversational AI, RAG, or agentic systems engineering position.

Role / Key Responsibilities

Design, develop, and deploy machine learning solutions that solve high-impact business problems.

Define modelling approaches, experimentation methodologies, and success metrics for AI initiatives.

Apply foundation-model embeddings and modern machine learning techniques to improve model performance and accelerate development.

Drive projects from problem definition through model development, deployment, and impact measurement.

Develop robust evaluation frameworks and benchmark new approaches against existing solutions.

Partner with business, product, engineering, and analytics teams to identify and prioritise opportunities.

Communicate technical findings and recommendations to both technical and non-technical stakeholders.

Contribute to the development of best practices, reusable assets, and modelling standards across the AI organisation.

Support and mentor junior data scientists through technical guidance and knowledge sharing.

All About You

Required Experience

Proven experience developing and deploying machine learning solutions in production environments.

Experience solving predictive modelling problems such as attrition, forecasting, recommendation systems, propensity modelling, fraud detection, risk modelling, or customer analytics.

Strong track record of delivering measurable business outcomes through machine learning.

Experience working in cross-functional teams to bring data science solutions from concept to deployment.

Required Technical Skills

Strong expertise in machine learning, predictive analytics, statistical modelling, and experimentation.

Advanced Python and SQL skills.

Experience with machine learning frameworks such as Scikit-Learn, XGBoost, LightGBM, TensorFlow, or PyTorch.

Strong understanding of classification, regression, forecasting, recommendation systems, ranking, clustering, and anomaly detection.

Experience with feature engineering, representation learning, embeddings, and downstream machine learning workflows.

Familiarity with transformer-based models and foundation-model applications.

Experience working with Databricks, Spark, Azure, AWS, or GCP.

Leadership & Communication

Strong analytical problem-solving skills.

Ability to influence stakeholders through technical expertise and data-driven recommendations.

Excellent communication and collaboration skills.

Ability to translate complex technical concepts into actionable business insights.

Minimum Qualifications

Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field.

5+ years of experience in machine learning, data science, AI, or advanced analytics.

Experience developing and deploying machine learning models in production environments.

Demonstrated experience applying statistical and machine learning techniques to real-world business problems.

Preferred Qualifications

Master's degree or PhD in Machine Learning, Artificial Intelligence, Computer Science, Statistics, Mathematics, or a related field.

Experience with foundation models, embeddings, or representation learning.

Experience in financial services, payments, banking, fintech, fraud, marketing analytics, or customer intelligence.

Publications, patents, conference presentations, or other evidence of technical thought leadership.

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.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

RagPyTorchTensorFlowscikit-learnXGBoostDatabricks

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
Walk me through how you've used PyTorch in your day-to-day work.
What are the limits of TensorFlow that you've run into, and how did you work around them?
What's a project where you used scikit-learn hands-on?
Walk me through how you've used XGBoost in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Rag, PyTorch, TensorFlow, scikit-learn, and XGBoost. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

Want your resume actually rewritten for this job?

The free preview above is everything we have today. A full resume rewrite is not live yet and has no price set. Join the waitlist and we will email you if we open it.