Build, evaluate, and deploy machine learning models for cybersecurity risk scoring, threat intelligence, and vendor risk assessment.
Perform exploratory data analysis and identify patterns, trends, and anomalies in large and complex datasets.
Evaluate and validate models, ensuring their accuracy, robustness, and scalability.
Collaborate with product managers and engineers to define data science requirements and integrate models into production systems.
Contribute to code reviews, design discussions, and data science best practices.
Stay current on new research and technologies to ensure we leverage state-of-the-art methods.
Communicate findings and recommendations effectively to both technical and non-technical audiences.

Requirements

Advanced degree in a quantitative field (e.g., Engineering, Computer Science, Statistics, Mathematics) or equivalent professional experience applying data science to complex problems.
5+ years of experience applying data science and machine learning to real-world challenges.
Proficient in Python and ML/data science frameworks (e.g., scikit-learn, XGBoost, MLFlow, Databricks).
Experience working with cloud-based data pipelines (AWS, GCP, or similar).
Solid understanding of ML algorithms, evaluation metrics, and model deployment.
Effective communicator and team player with a passion for solving hard problems.
Prior exposure to cybersecurity, threat intelligence, or risk scoring a significant plus.
Prior exposure and interest in LLMs is a plus.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningdata scienceexploratory data analysisPythonscikit-learnXGBoostMLFlowDatabricksML algorithmsmodel deployment

Soft Skills

effective communicatorteam playerproblem solving