Prinicipal Data Scientist

About This Role

We are seeking a hands-on Principal Data Scientist to lead applied machine learning and research initiatives for security- and privacy-critical AI systems. This role is ideal for a senior practitioner who combines deep theoretical understanding with strong engineering execution, and who has experience building ML systems within strict compliance, privacy, and regulatory boundaries.

The role spans research-driven exploration and production delivery. You will allocate and manage your technical bandwidth across exploratory research, model development, and strategic mentorship—ensuring that advanced techniques translate into robust, deployable, and compliant solutions. You will work extensively with NLP and Transformer-based architectures, alongside classical ML methods, to solve real-world security problems at scale.

**This is not a purely academic or managerial role.

You will design experiments, write code, review models, evaluate risks, and ship systems, while also guiding technical direction and elevating the team’s capabilities.**

Key Responsibilities :

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

Required Qualifications:

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