Prinicipal Data Scientist

Overview

Applied Research & Strategic Direction

• Define research agendas aligned with building a security-first AI platform
• Allocate bandwidth across near-term production improvements, mid-term architectural exploration, and longer-term research bets
• Conduct structured experimentation on detection accuracy, adversarial robustness, privacy impact, and regulatory compliance
• Translate research into engineering-ready designs and contribute to external visibility (publications, whitepapers, conferences where permissible)

Model Development & Implementation

• Architect and deploy end-to-end ML solutions using classical ML, deep learning, and Transformer architectures
• Lead hands-on development: data exploration, feature design, training, hyperparameter optimization, error analysis
• Build models for real-world constraints: latency, throughput, scalability, cost efficiency, and robustness under distribution shift
• Apply privacy-preserving techniques: differential privacy, secure aggregation, controlled data access

Evaluation & Monitoring

• Design rigorous evaluation frameworks for security-sensitive ML systems
• Define offline/online benchmarks measuring precision/recall trade-offs and business impact
• Establish monitoring systems tracking model performance, data quality, bias, fairness, and privacy

Security, Privacy & Compliance

• Partner with legal, privacy, and security teams to ensure systems operate within compliance boundaries
• Embed responsible AI principles and ensure interpretability for internal reviews, regulatory audits, and customer explanations
• Anticipate and mitigate adversarial scenarios relevant to security-focused ML

Mentorship & Technical Leadership

• Provide hands-on mentorship to data scientists and ML engineers
• Review model designs, experiments, and code for correctness, robustness, and security implications

Education & Experience

• PhD in Computer Science, Statistics, Mathematics, or related quantitative field OR Master's + 8+ years relevant industry experience
• 10+ years experience in data science, machine learning, or applied research with 3+ years in lead/lead/principle-level role
• Proven track record shipping ML systems into production

Core Technical Competencies

• Deep, hands-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: Python, PyTorch/TensorFlow, NumPy, Pandas, SciPy, Scikit-learn
• Experience with model interpretability (SHAP, LIME) and responsible AI practices
• Strong MLOps familiarity: Git, CI/CD, Docker, model deployment and monitoring
• Nice to have
• Publication 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)
• Familiarity with model optimization (quantization, efficient inference)
• Direct experience with compliance, privacy, or regulatory teams