Principal Data Scientist

Lead applied machine learning and research initiatives for security- and privacy-critical AI systems. Responsible for designing, developing, and deploying ML solutions while ensuring compliance, privacy, and regulatory standards. This is a hands-on role combining research, engineering, and strategic mentorship to translate advanced techniques into robust, deployable, and secure solutions.Key ResponsibilitiesApplied Research & Strategic DirectionDefine research agendas aligned with building a security-first AI platform.Allocate effort across near-term production improvements, mid-term architecture exploration, and long-term research initiatives.Conduct structured experimentation on detection accuracy, adversarial robustness, privacy impact, and regulatory compliance.Translate research into engineering-ready designs and contribute to external visibility via publications or whitepapers where permissible.Architect and deploy end-to-end ML solutions using classical ML, deep learning, and Transformer architectures.Lead hands-on development: data exploration, feature engineering, training, hyperparameter tuning, and error analysis.Build models that meet real-world constraints: latency, throughput, scalability, cost efficiency, and robustness under distribution shifts.Apply privacy-preserving techniques, including differential privacy, secure aggregation, and controlled data access.Evaluation & MonitoringDesign rigorous evaluation frameworks for security-sensitive ML systems.Define offline and online benchmarks measuring precision/recall trade-offs and business impact.Establish monitoring systems for model performance, data quality, bias, fairness, and privacy compliance.Security, Privacy & CompliancePartner with legal, privacy, and security teams to ensure ML systems operate within compliance boundaries.Embed responsible AI principles and ensure interpretability for internal reviews, audits, and customer explanations.Anticipate and mitigate adversarial scenarios relevant to security-focused ML.Mentorship & Technical LeadershipProvide hands-on mentorship to data scientists and ML engineers.Review model designs, experiments, and code for correctness, robustness, and security implications.Guide technical direction and elevate the team’s capabilities in applied ML and secure AI development.QualificationsEducation & ExperiencePhD in Computer Science, Statistics, Mathematics, or related quantitative field OR Master’s with 8+ years relevant industry experience.10+ years experience in data science, machine learning, or applied research, with 3+ years in lead/principal-level roles.Proven track record of shipping ML systems into production.Core Technical CompetenciesHands-on experience with Transformer architectures (NLP-focused) and classical ML algorithms.Strong understanding of generalization, bias–variance trade-offs, experimental design, and statistical rigor.Practical experience with privacy-preserving ML techniques.Proficiency in Python, PyTorch/TensorFlow, NumPy, Pandas, SciPy, Scikit-learn.Experience with model interpretability (SHAP, LIME) and responsible AI practices.Familiarity with MLOps: Git, CI/CD, Docker, model deployment, and monitoring.Nice to HavePublication record in top-tier ML venues.Experience with Generative AI/LLMs in constrained or enterprise environments.Prior work on security-focused ML (fraud, abuse, spam, threat detection).Experience with model optimization (quantization, efficient inference).Direct experience collaborating with compliance, privacy, or regulatory teams.Soft SkillsExcellent leadership and mentorship capabilities.Strong analytical thinking, problem-solving, and attention to detail.Effective communication and collaboration skills across technical and non-technical teams.Ability to balance research exploration with production delivery under high standards of security and compliance.You are applying for Principal Data Scientist#J-18808-Ljbffr