Lead Data Scientist

Join our Cyber Analytics team as a Lead Data Scientist. In this leadership role, you will develop advanced analytics and machine learning solutions across cybersecurity, payments, and fraud. You will also manage junior team members, helping them grow their technical capabilities and deliver high-quality work. Your responsibilities will include designing and implementing analytical solutions, providing technical leadership for graph analytics, and translating complex problems into clear analytical approaches. You will work closely with various teams and communicate findings to both technical and non-technical stakeholders.

- Lead the design, development, validation, and implementation of advanced analytical solutions, predictive models, and machine learning algorithms using large-scale payment, fraud, merchant, cybersecurity, and threat-intelligence data. - Provide technical leadership for graph analytics and modeling, including graph schema design, relationship modeling, community detection, link analysis, graph embeddings, and graph neural networks. - Translate ambiguous business, product, cybersecurity, and fraud problems into clearly defined analytical approaches, technical requirements, and delivery plans.

- You are confident working independently while maintaining the humility to seek additional expertise, consider alternative approaches, and incorporate feedback - You have proven experience leading data science projects from problem definition and exploratory research through validation, implementation, and production handoff - Bachelor’s or Master’s degree in Data Science, Computer Science, Cybersecurity, Information Technology, Engineering, Mathematics, Statistics, Physics, or a related quantitative field - You can translate complex technical results into clear business insights and communicate effectively and persuasively with both technical and non-technical audiences - You are fundamentally a problem-solver who is comfortable entering complex and evolving problem spaces, identifying the most important questions, and developing practical analytical solutions - You are a strong collaborator who can work across data science, product, engineering, cybersecurity, and operations teams - You are organized, accountable, and comfortable managing multiple priorities in an environment where analytical requirements and technical architecture continue to evolve - You understand how to determine when a graph-based approach provides meaningful value over conventional relational, tabular, or transaction-level modeling - Strong knowledge of statistical modeling, supervised and unsupervised machine learning, anomaly detection, feature engineering, model evaluation, and model-performance monitoring - Substantial hands-on experience with Apache Spark, Hadoop, and Databricks, including processing and analyzing large, distributed datasets - Advanced proficiency in Python and SQL - The candidate must bring substantial hands-on knowledge of graph analytics. This includes designing graph data models, engineering node and edge features, and developing graph-based machine learning solutions at scale - Knowledge of web application security, common cyber vulnerabilities, threat vectors, attack techniques, payment-card fraud, and methods used to compromise or monetize payment credentials is helpful - Experience with graph databases or graph query languages is valuable, but the ability to implement scalable graph analytics outside a dedicated graph database is particularly relevant to this role