Senior Data Scientist

Start free →

Or apply yourself on the official page →

About the role

As a Senior Data Scientist you will lead research, development and client-focused innovation across our fraud analytics portfolio by creating solutions that strengthen fraud detection, digital trust and risk decisioning across markets. You will evaluate and integrate data assets while balancing multiple concurrent projects, anticipating risks and deliver outcomes. You will invent machine learning, graph, behavioural and GenAI methods to structured and unstructured data; and convert prototypes into reusable capabilities on the Ascend platform. The product scope includes fraud analytics and decisioning solutions, unified digital intelligence, device fingerprinting and behavioural intelligence, trade-delete and credit-washing analytics, GenAI fraud assistants. You will report to Director of Data Science and GenAI. What you'll do: • Analyze large-scale fraud, credit, device, behavioral and digital-interaction data to identify risk and trust signals. • Develop fraud models, scores, attributes, device profiles and graph-based capabilities for AFS and related solutions. • Apply advanced machine learning, graph analytics, deep learning and GenAI methods to complex fraud challenges. • Evaluate and measure value of new data assets. • Build scalable data pipelines, reusable analytical tools, automation templates and production-ready solutions on Ascend. • Validate model performance, stability, fairness, explainability and growth. • Lead analytical and productization workstreams. • Apply advanced algorithms to business problems and move solutions from research or prototype into production or client use. • Translate technical findings into recommendations. • Partner across multiple geographically distributed teams. • Mentor colleagues, share expertise and promote responsible AI and reproducible practices. • Maintain knowledge of fraud trends, digital intelligence, regulation, GenAI and latest analytical technologies. • Measure benefits and explain trade-offs. What you'll bring: • 5–7+ years of relevant experience in data science, AI, predictive modeling or advanced analytics, including ownership of complex, hands-on innovation and client-focused projects. • 7+ years of experience developing data and automation pipelines and transitioning analytical prototypes into monitored, scalable production solutions. • Grasp of probability, statistical inference, optimization, linear algebra and calculus • Understanding of when and how to apply regression, machine learning, AI, deep learning, clustering, gradient boosting, graph algorithms, anomaly and pattern detection, and Gen AI. • Command of model validation, including cross-validation, regularization, Bayesian methods, in-sample versus out-of-sample testing, statistical significance tests and Monte Carlo simulation. • Write near-production-level Python code; experience with PySpark and distributed data processing. • Knowledge in modern ML and deep-learning tooling and LLM and agent-based development frameworks. • Understanding of large-scale data technologies, cloud platforms, NoSQL and graph databases, time-series data and tools for unstructured information. • Knowledge of fraud risk, identity, device fingerprinting, digital or behavioral intelligence, credit-washing analytics, credit risk, fraud graphs or fraud strategy optimization. • Understanding of model governance, responsible AI, explainability, privacy, security and regulatory considerations relevant to fraud and digital-interaction data. • Interest and experience mentoring colleagues and providing technical leadership. • Advanced technical/quantitative degree or equivalent experience. • Fluent English What you will get: • Personal Development - career pathway for professional growth supported by learning and dev

View the official posting →