Lead Data Scientist, AI Engineering

Overview

Job Title:

Lead Data Scientist, AI Engineering

Overview:

Lead 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 Lead Data Scientist, AI Engineering to lead the development of advanced machine learning solutions across domains. This role combines deep expertise in predictive modelling, experimentation, and technical leadership to deliver measurable business impact.

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 leadership role rather than a conversational AI, RAG, or agentic systems engineering position.

Role / Key Responsibilities

Lead the design, development, and deployment of machine learning solutions that solve high-impact business problems.

Define modelling approaches, experimentation frameworks, 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 deployment and business impact measurement.

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

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

Present technical findings and recommendations to stakeholders and senior leadership.

Mentor and develop data scientists and AI engineers through technical guidance, reviews, and coaching.

Contribute to hiring, capability development, and the long-term technical direction of the AI organisation.

All About You

Required Experience

Proven experience leading machine learning projects from concept through production deployment.

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 leading technical teams, mentoring practitioners, and influencing technical direction.

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 problem-solving and decision-making skills.

Ability to lead through influence across cross-functional teams.

Excellent communication and stakeholder management capabilities.

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.

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

Experience developing and deploying machine learning models in production environments.

Experience leading technical projects or teams.

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.

To find salary ranges and other disclosures for US and Europe countries where applicable, visit https://hrportal.ehr.com/mastercard/Home/Compensation/Compensation/more#. In the US, see link for “salary structures”. For more information on benefits, visit People Place and review the tabs for Benefits and for Time Off & Leave.

Company:

Mastercard

Qualifications:

Language requirements:

Specific requirements:

Educational level:

Level of experience (years):

Senior (5+ years of experience)

Tagged as: Classification, Clustering, Industry, Ireland, NLP, Predictive Analytics