Senior Cyber Data Scientist

Data Scientist / Machine Learning Engineer (Remote Contract) Overview We are looking for an experienced Data Scientist / Machine Learning Engineer to support development of analytical models, machine learning solutions, and data-driven insights for enterprise cybersecurity and operational analytics use cases. The ideal candidate has strong experience in statistics, mathematical modeling, machine learning, Python-based analytics, and working with large datasets. This role requires hands-on experience building, evaluating, and improving ML models, not only using data platforms or visualization tools. Responsibilities * Develop and evaluate machine learning models for classification, prediction, anomaly detection, and risk analytics. * Apply statistical modeling techniques to identify patterns, trends, and relationships within large datasets. * Perform exploratory data analysis (EDA), feature engineering, model selection, and performance evaluation. * Build scalable data processing and analytical workflows using Python and modern data science frameworks. * Work with relational databases and structured data sources to extract, transform, and analyze information. * Develop cybersecurity analytics solutions such as: * User behavior analytics (UBA) * Threat/anomaly detection * Risk scoring models * Security event pattern analysis * Automated analytical workflows * Create clear visualizations, dashboards, and quantitative summaries for technical and non-technical stakeholders. * Document modeling approaches, assumptions, limitations, and results. * Collaborate with engineering teams to integrate models into production workflows. Required Skills * 5+ years of Data Science / Machine Learning experience. * Strong Python programming skills. * Strong understanding of: * Statistics * Probability * Mathematical modeling * Machine learning algorithms * Model evaluation techniques * Experience with: * Scikit-learn * Pandas * NumPy * TensorFlow or PyTorch * SQL * Experience working with large datasets and relational databases. * Knowledge of ML lifecycle: * Data preparation * Feature engineering * Training * Validation * Deployment * Monitoring Preferred Experience * Cybersecurity analytics experience. * Experience with SIEM/security datasets (Splunk, Sentinel, Elastic, etc.). * Experience with anomaly detection and behavioral analytics. * Familiarity with Risk Management Framework (RMF) concepts. * Experience deploying models in cloud environments (AWS/Azure/GCP). * Experience with ML pipelines and MLOps tools. Example Project Experience Candidates should be able to explain: * A machine learning model they built from raw data to production. * How they selected algorithms and features. * How they handled class imbalance, overfitting, and model drift. * How they measured model performance. * Challenges encountered and how they improved results. Deliverables * Data analysis reports * Machine learning models * Python notebooks/scripts * Model documentation * Performance evaluation reports * Technical recommendations Engagement * Remote contract * Flexible schedule * Long-term collaboration possible * Candidates should provide examples of previous data science and ML projects