M
Mastercard
Lead Data Scientist (AI Engineering)
Skills
Google Cloud PlatformSQL QueriesPythonData SciencePyTorchTensorFlowDatabricksData AnalysisMachine LearningAIAWSWorkdayAzure
2 months ago
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Important information
Contract type:
Permanent
Location:
Ireland
Work mode:
Onsite
Published on:
10 September 2026
What they need
- 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
- 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
- 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- Experience leading technical teams, mentoring practitioners, and influencing technical direction
- Strong track record of delivering measurable business outcomes through machine learning
- 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
- Experience with machine learning frameworks such as Scikit-Learn, XGBoost, LightGBM, TensorFlow, or PyTorch
- Experience developing and deploying machine learning models in production environments
- Advanced Python and SQL skills
- 8+ years of experience in machine learning, data science, AI, or advanced analytics
- Experience leading technical projects or teams
- Strong problem-solving and decision-making skills
- Ability to lead through influence across cross-functional teams
- Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field
- Familiarity with transformer-based models and foundation-model applications
- Experience working with Databricks, Spark, Azure, AWS, or GCP
- Ability to translate complex technical concepts into actionable business insights
- Strong understanding of classification, regression, forecasting, recommendation systems, ranking, clustering, and anomaly detection
- Strong expertise in machine learning, predictive analytics, statistical modelling, and experimentation
- Experience with feature engineering, representation learning, embeddings, and downstream machine learning workflows
- Excellent communication and stakeholder management capabilities
- 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- End Date: January 1, 2027
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