BeyondTrust
cyber security SaaS
StaffAIDataScientist
Neural analysis suggests this role is
optimal for Senior candidates.
“Staff AI Data Scientist at BeyondTrust. Skills: design, build, and deploy machine learning models and AI-driven solutions, production use cases, fine-tune, prompt-engineer, and evaluate large language models, statistical modeling, experimental design, evaluation methodology. Design, build, and deploy machine learning models and AI-driven solutions that solve complex business problems. Develop and deploy machine learning models (supervised, unsupervised, deep learning) for production use cases”
What You'll Achieve.
creating a safer world through our cyber security SaaS portfolio; turning raw data into actionable insights and intelligent systems that ship to production; define success metrics; track model performance, drift, and fairness over time
Industry & Context.
solve complex business problems; translate ambiguous problems into well-scoped modeling solutions
What They're Looking For.
Must Have
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, proficiency in Python, 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
Nice to Have
Background in cybersecurity, identity security, or anomaly detection, Familiarity with adversarial ML or AI safety research, Contributions to open-source projects or published research, 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
What You'll Do.
and deploy machine learning models and AI-driven solutions that solve complex business problems
Develop and deploy machine learning models (supervised
deep learning) for production use cases
define success metrics
and run rigorous offline and online evaluations (A tests
and evaluate large language models for domain-specific tasks
Build monitoring and evaluation frameworks to track model performance
and fairness over time
How You'll Work.
Team & Collaboration
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; Communicate findings and recommendations to both technical and non-technical stakeholders; Mentor other data scientists and help raise the bar for modeling rigor across the team
Communication Scope
Communicate findings and recommendations to both technical and non-technical stakeholders through clear visualizations and written reports
Process & Methodology
translate ambiguous problems into well-scoped modeling solutions
Full Job Description
BeyondTrust is a place where you can bring your purpose to life through the work that you do, creating a safer world through our cyber security SaaS portfolio. Our culture of flexibility, trust, and continual learning means you will be recognized for your growth, and for the impact you make on our success. You will be surrounded by people who challenge, support, and inspire you to be the best version of yourself. The Role We're looking for a Staff AI Data Scientist to design, build, and deploy machine learning models and AI-driven solutions that solve complex business problems. You'll work at the intersection of applied research, software engineering, and product — turning raw data into actionable insights and intelligent systems that ship to production. What You’ll Do 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 What You’ll Bring 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, sc
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