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Lead Data Scientist, AI Engineering at Philippines: Mastercard Transaction Services (Philippines) Inc. · Katori

Asks for

The ad says“Strong understanding of classification, regression, forecasting, recommendation systems, ranking, clustering, and anomaly detection.”
The ad says“Experience working with Databricks, Spark, Azure, AWS, or GCP.”
The ad says“Experience working with Databricks, Spark, Azure, AWS, or GCP.”
The ad says“Strong understanding of classification, regression, forecasting, recommendation systems, ranking, clustering, and anomaly detection.”
The ad says“Strong understanding of classification, regression, forecasting, recommendation systems, ranking, clustering, and anomaly detection.”
The ad says“Experience working with Databricks, Spark, Azure, AWS, or GCP.”
The ad says“Experience with feature engineering, representation learning, embeddings, and downstream machine learning workflows.”
The ad says“Strong expertise in machine learning, predictive analytics, statistical modelling, and experimentation.”
The ad says“Experience with feature engineering, representation learning, embeddings, and downstream machine learning workflows.”
The ad says“Strong understanding of classification, regression, forecasting, recommendation systems, ranking, clustering, and anomaly detection.”
The ad says“Experience working with Databricks, Spark, Azure, AWS, or GCP.”
The ad says“Experience with machine learning frameworks such as Scikit-Learn, XGBoost, LightGBM, TensorFlow, or PyTorch.”
The ad says“Strong expertise in machine learning, predictive analytics, statistical modelling, and experimentation.”
The ad says“Strong expertise in machine learning, predictive analytics, statistical modelling, and experimentation.”
The ad says“Advanced Python and SQL skills.”
The ad says“Experience with machine learning frameworks such as Scikit-Learn, XGBoost, LightGBM, TensorFlow, or PyTorch.”
The ad says“Strong understanding of classification, regression, forecasting, recommendation systems, ranking, clustering, and anomaly detection.”
The ad says“Strong understanding of classification, regression, forecasting, recommendation systems, ranking, clustering, and anomaly detection.”
The ad says“Experience with feature engineering, representation learning, embeddings, and downstream machine learning workflows.”
The ad says“Experience with machine learning frameworks such as Scikit-Learn, XGBoost, LightGBM, TensorFlow, or PyTorch.”
The ad says“Experience working with Databricks, Spark, Azure, AWS, or GCP.”
The ad says“Advanced Python and SQL skills.”
The ad says“Strong expertise in machine learning, predictive analytics, statistical modelling, and experimentation.”
The ad says“Experience with machine learning frameworks such as Scikit-Learn, XGBoost, LightGBM, TensorFlow, or PyTorch.”
The ad says“Experience with machine learning frameworks such as Scikit-Learn, XGBoost, LightGBM, TensorFlow, or PyTorch.”

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

Lead Data Scientist, AI Engineering

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.

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.