Principal Data Scientist

Responsibilities

• Define 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.
• Design 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.
• Partner 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.
• Provide 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.

Requirements

• PhD 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.
• 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 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.
• Excellent 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.

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).
• Experience with model optimization (quantization, efficient inference).
• Direct experience collaborating with compliance, privacy, or regulatory teams.