Lead Data Scientist leading AI and ML initiatives for fraud detection and risk intelligence within Mastercard’s Security Solutions portfolio.
Responsibilities
Lead and directly contribute to the design, development, deployment, and optimization of machine learning models for fraud detection, risk scoring, anomaly detection, and related security applications.
Define, build, and guide scalable AI and ML pipeline architecture supporting model training, validation, deployment, monitoring, retraining, and continuous improvement.
Hands‑on design and implementation of Generative AI and Agentic AI solutions leveraging LLMs, RAG, reasoning and planning workflows, Chain‑of‑Thought prompting, tool/function calling, and multi‑agent orchestration.
Work with large‑scale transactional datasets using distributed computing technologies such as Spark and Databricks.
Implement MLOps best practices, including CI/CD, model governance, observability, experimentation frameworks, and performance monitoring.
Contribute to New York Life's digital transformation by developing and deploying AI and machine learning solutions that enhance efficiency and improve client, agent, and employee experiences.
Execute AI/ML initiatives in partnership with data, technology, product and business teams.
Deliver business-aligned OKRs, ensuring AI initiatives are outcome-driven and tied to enterprise value creation.
Contribute to the end-to-end model lifecycle: data exploration, feature engineering, model training/validation, deployment, monitoring, and iteration.
Design, build, and validate ML models across supervised and unsupervised paradigms (e.g., classification, regression, survival analysis, clustering).
Apply statistical methods and experimentation techniques — including hypothesis testing, causal inference, and A/B testing — to validate model performance.
Develop, implement, and support AI computational and data science solutions—including modelling algorithms, machine learning models, and AI tools—tailored to diverse scientific and business application areas.
Translate complex, unstructured business or scientific problems into appropriate data-driven analytical and computational solutions.
Collaborate with cross-functional stakeholders—including scientists, data scientists, engineers, and business teams—to identify opportunities, understand challenges, and deliver impactful AI-driven recommendations and insights.
Research and develop new predictive models, computational methods, and algorithms, applying techniques such as classification, regression, clustering, natural language processing (NLP), image analysis, graph simulation theory, deep learning, and recommendation systems as needed.
Integrate, process, and analyze data from multiple sources and modalities to ensure comprehensive approaches to problem-solving.
Requirements
Bachelor's
Master's
PhD
Nice to have: Master’s or PhD in a relevant quantitative discipline
Nice to have: publishing predictive modelling results
As an Operations Research/Data Scientist on the Assignments team, you will help build, maintain, and scale the algorithms/models that power our global delivery network.
Responsibilities
Build, maintain, and scale algorithms/models for the global delivery network
Collaborate with senior scientists and software engineers to translate business rules into mathematical formulations
Build predictive models that inform dynamic parameters
Seeking a Lead Data Scientist to provide senior scientific leadership for Data Science and applied-model development within Digital Engineering, Digital & IT.
Locations (2)
Newbury, Berkshire, United Kingdom
Paddington, London, United Kingdom
Responsibilities
Frame customer, commercial and operational challenges as testable Data Science questions, defining outcomes, baselines, assumptions, decision thresholds and measures of value.
Lead the development and validation of predictive, forecasting, propensity, anomaly and fraud, optimisation and causal models for prioritised use cases.
Define appropriate experimental design, sampling approaches, feature strategies, evaluation methods and statistical tests based on the decision being supported and the available data.
Lead modelling, experimentation and scientific validation, ensuring analytical approaches are proportionate to the problem and evidence available.
Set and assure scientific standards for reproducibility, peer review, explainability, fairness, bias, uncertainty and model limitations.
Build statistical and machine learning models, conducting natural language processing (NLP), and developing automated solutions for complex business challenges.
Construct and execute complex database search queries across multiple databases using SQL and API interfaces.
Perform web scraping and apply various techniques for processing unstructured data.
Leverage automation and machine learning to manage data, predict scenarios, and make data-driven recommendations.
Learn and leverage technologies including AWS and Git.
This role is based in Barcelona, with an on-site commitment of three days a week. Fluency in English is required. The Frontier AI Research lab is a group of researchers working within the Enterprise AI unit. Working at the bleeding edge of AI and ML method you'll keep AZ at the forefront of AI in our industry. You will leverage advanced quantitative, computational, and algorithmic expertise to develop, deliver, and support scalable AI and data science solutions across AstraZeneca’s business areas.
Responsibilities
Develop, implement, and support AI computational and data science solutions—including modelling algorithms, machine learning models, and AI tools—tailored to diverse scientific and business application areas.
Translate complex, unstructured business or scientific problems into appropriate data-driven analytical and computational solutions.
Collaborate with cross-functional stakeholders—including scientists, data scientists, engineers, and business teams—to identify opportunities, understand challenges, and deliver impactful AI-driven recommendations and insights.
Research and develop new predictive models, computational methods, and algorithms, applying techniques such as classification, regression, clustering, natural language processing, image analysis, graph simulation theory, deep learning, and recommendation systems as needed.
Integrate, process, and analyze data from multiple sources and modalities to ensure comprehensive approaches to problem-solving.
Requirements
Bachelor’s, Master’s or PhD Degree—in Mathematics, Computer Science, Engineering, Physics, Statistics, Computational Sciences, or a related quantitative discipline—or equivalent experience.
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