Staff AI Data Scientist

BeyondTrust•Toronto, ON

About The Position

BeyondTrust is looking for a Staff AI Data Scientist to design, build, and deploy machine learning models and AI-driven solutions that solve complex business problems. This role operates at the intersection of applied research, software engineering, and product, focusing on transforming raw data into actionable insights and intelligent systems that are deployed into production. The position is within a culture that values flexibility, trust, and continuous learning, where employees are recognized for their growth and impact.

Requirements

Master's or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field (or equivalent practical experience)
3+ years of hands-on experience building and deploying ML models in production environments
Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, scikit-learn, and Hugging Face Transformers
Deep understanding of statistical modeling, experimental design, and evaluation methodology
Experience with cloud platforms (AWS, GCP, or Azure) for training, serving, and scaling models
Familiarity with LLM fine-tuning techniques (LoRA, RLHF, instruction tuning) and serving infrastructure
Experience leveraging AI coding assistants (such as Claude Code, OpenCode, or GitHub Copilot) to accelerate development workflows

Nice To Haves

Background in cybersecurity, identity security, or anomaly detection
Familiarity with adversarial ML or AI safety research
Contributions to open-source projects or published research

Responsibilities

Develop and deploy machine learning models (supervised, unsupervised, deep learning) for production use cases
Design experiments, define success metrics, and run rigorous offline and online evaluations (A/B tests, holdouts, causal analyses)
Fine-tune, prompt-engineer, and evaluate large language models for domain-specific tasks
Partner with data engineering to define the features, datasets, and pipelines needed for model training and inference
Collaborate with engineering, product, and business teams to translate ambiguous problems into well-scoped modeling solutions
Build monitoring and evaluation frameworks to track model performance, drift, and fairness over time
Communicate findings and recommendations to both technical and non-technical stakeholders through clear visualizations and written reports
Mentor other data scientists and help raise the bar for modeling rigor across the team