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Strong global brand, metro location, and common junior data-science role attract dense competition.
Core ML/Python skills are transferable but fraud and graph specialization increase domain sensitivity.
Explicit minimum experience plus mandatory ML, PySpark, graph, and cloud skills.
Support development of fraud risk management and business intelligence analytic solutions for clients.
Develop fraud detection models using graph technology and analyze large-scale datasets (petabytes) to build meaningful model solutions.
Deliver analytic insights and recommendations to internal/external customers and executives; identify customer strategy opportunities to adopt analytic products and services.
Bachelor’s degree in statistics, applied mathematics, financial mathematics, engineering, operations research, or related quantitative field.
Minimum 1 year professional analytic experience in SaaS, Consulting, or related industries; experience in Financial Services preferred.
Proficient in Python and PySpark with experience in advanced machine learning methods and LLMs usage.
Experience with machine learning, deep learning, graph-based modeling for fraud risk, and cloud platforms such as GCP, AWS, or Azure working with large distributed datasets.
Experience working with fraud analytics using graph and unsupervised modeling techniques in global organizations.
Strong skills implementing NLP techniques ranging from statistical methods to transformer models and familiarity with large language models (LLMs).
Comfortable operating in hybrid work environments and delivering high-impact analytic presentations to both technical and executive stakeholders.