Staff Data Scientist - Digital Intelligence

What you'll do

• Lead machine-learning and feature-development initiatives using device, network, browser, mobile, session, and behavioral data.
• Develop production risk signals and evaluation methods addressing data quality, fraud tradeoffs, drift, and adversarial behavior.
• Influence telemetry and production standards, communicate technical recommendations, and mentor data scientists.

What they ask for

• Master’s or Ph.D. in a quantitative field such as Computer Science, Machine Learning, Statistics, Mathematics, or Data Science.
• 12+ years of experience in data science, applied machine learning, statistical modeling, or related technical roles.
• Experience building, deploying, validating, and improving production ML models, risk signals, or decisioning systems; strong background in an adversarial data domain.
• Expert-level SQL; strong Python and distributed data processing experience; deep knowledge of ML, feature engineering, evaluation, monitoring, and statistical validation.

Nice to have

• Experience in device intelligence, fingerprinting, behavioral biometrics, network intelligence, or related risk signals.
• Experience with streaming or low-latency systems, adversarial or responsible ML, ML frameworks, or production ML observability.

Pay

$210K – $250K USD base salary; offers equity; offers bonus (amounts not stated)

Tools

SQL, Python, Spark, PySpark, scikit-learn, XGBoost, TensorFlow, PyTorch

AI focus

Develops and evaluates production machine-learning fraud and identity risk signals from digital telemetry, including robustness to adversarial behavior.