WHAT YOU'LL DO
Design and deploy advanced machine learning systems for device identification, anomaly detection, and fraud prevention—balancing precision, recall, and real-world adversarial dynamics.
Contribute to the development of scalable data pipelines and production ML workflows using structured and unstructured telemetry (e.g., browser, mobile, session data).
Investigate high-complexity signals (e.g., emulator use, spoofing, low-entropy fingerprints), applying advanced statistical methods and domain knowledge to detect fraud and abuse.
Translate ambiguous business problems into modeling approaches, using a combination of supervised, unsupervised, and heuristic techniques.
Partner with engineering, product, and risk teams to contribute to data architecture decisions, signal collection, and planning.
Drive experimental design, A/B testing frameworks, and robust validation techniques to ensure model generalizability and long-term trust.
Contribute to team standards for ML explainability, risk evaluation, and feature logging.
Document methodologies and communicate results effectively through dashboards, presentations, and reports for both technical and executive audiences.
Mentor junior data scientists and participate in cross-functional working groups.
WHAT YOU BRING
Master’s degree (or equivalent practical experience) in Computer Science, Machine Learning, Statistics, or a related quantitative field.
6+ years of experience in data science or applied machine learning, including experience working in production environments.
Excellent SQL skills and extensive experience with large-scale databases and data modeling.
Proven track record of deploying and maintaining ML models in live systems, ideally involving streaming or near-real-time data.
Proficiency in Python and distributed computing tools (e.g., Spark, PySpark).
Hands-on experience with ML frameworks such as scikit-learn, XGBoost, TensorFlow, or similar.
Excellent communication skills—able to explain complex technical results to non-technical stakeholders and senior leadership.
Experience designing and interpreting experiments, working with real-world noisy datasets, and applying sound validation techniques to assess model robustness.
Demonstrated ability to break down ambiguous problems, apply analytical rigor, and uncover meaningful insights that influence product or risk strategies.
Strong judgment across data quality, model selection, and business impact tradeoffs.
Collaborative mindset and experience working cross-functionally with product, engineering, and analytics teams.
PREFERRED QUALIFICATIONS
Background in fraud detection, behavioral biometrics, anomaly detection, or adversarial modeling.
Experience with high-cardinality feature engineering techniques (e.g., frequency/target encoding, embeddings).
Familiarity with privacy-preserving or robust ML techniques.
Knowledge of browser/mobile fingerprinting, VPN/proxy detection, or telemetry signal processing.
WHAT YOU’LL GAIN
Hands-on experience with real-world data science challenges in a high-impact industry.
A collaborative and inclusive work environment that fosters learning and growth.
Opportunities to grow into staff-level or technical leadership roles over time.
- Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
- If you need an accommodation during any stage of the application or hiring process—including interview or onboarding support—please reach out to your Socure recruiting partner directly.
Follow Us!
YouTube https://www.youtube.com/c/Socure | LinkedIn https://www.linkedin.com/company/socure/ | X (Twitter) https://x.com/socureme | Facebook https://www.facebook.com/socure/
