Data Scientist - Research Team

Surf AI is the agentic operations platform for enterprise security teams. We don't just surface risk, we close it. Our platform connects context across identity, cloud, HR, IT, and SaaS systems, and uses specialized AI agents to drive remediation end-to-end, with human oversight at every step.

We're backed by Accel, Cyberstarts, and Boldstart Ventures, and trusted by Fortune 500 enterprises already deploying Surf in production.

Our team is small and senior, with deep roots in identity, security, and enterprise infrastructure. We work at the intersection of agentic AI and applied security - and we take seriously what it means to build systems that act in real enterprise environments.

Who are we looking for?

We're looking for a Data Scientist to join our Research Team. In this role, you will research and develop production-grade models and data pipelines that transform raw enterprise signals into high-fidelity, actionable insights. You will own the full lifecycle of model development - from experimentation to deployment and monitoring - in service of our AI-powered platforms that drive mission-critical decisions.

What you'll do

• Production ML Modeling - Design, build, and iterate on scalable statistical, heuristic and machine learning models using supervised and unsupervised techniques to uncover patterns, anomalies, and predictive signals across enterprise-scale datasets.
• Feature Engineering & Enrichment - Design meaningful features by enriching raw data with organizational, temporal, and environmental context to improve model performance.
• Model Deployment & Monitoring - Deploy models to production using modern MLOps practices; monitor performance, detect drift, and retrain as needed to maintain reliability and accuracy.
• Cross-Functional Collaboration - Work closely with Software Engineers, Product Managers, and domain experts to ensure models are integrated into applications and workflows with measurable impact.
• Research & Innovation - Explore emerging techniques in ML, data representation, and weak supervision to improve model generalization, interpretability, and signal fidelity.
• Cloud & API Integration - Leverage cloud-native tools (AWS, Azure, GCP) and APIs to scale model inference, automate retraining, and integrate predictions into end-user applications.
• Data Pipeline Engineering - Build robust, automated data pipelines (ETL/ELT) that prepare and structure data for modeling and serve features into production systems.

Qualifications & Skills

• 3+ years of experience building and deploying machine learning models in production environments.
• Expert in Python and ML libraries such as Scikit-learn, XGBoost, LightGBM, or TensorFlow/PyTorch.
• Strong understanding of data structures, algorithms, and software engineering fundamentals.
• Proficient in SQL and experience with distributed computing frameworks (e.g., Spark, Dask).
• Experience with feature stores, ML pipelines, and workflow orchestration tools (e.g., Airflow, Prefect).
• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related technical field, or equivalent practical experience or training.
• Ability to break down complex problems and communicate technical solutions clearly to diverse stakeholders.

Bonus Experience

• Experience integrating models into backend systems or real-time applications.
• Familiarity with monitoring and observability platforms (e.g., Prometheus, Grafana, Splunk).
• Exposure to graph-based machine learning, time-series forecasting, or semi-supervised learning.
• Understanding of IAM, compliance frameworks, or secure machine learning practices.
• Experience incorporating model outputs into GenAI or LLM-enabled workflows.

Why Join Us?
If you want to work on foundational systems, ship AI into production, and help define how agentic security actually operates, this is an opportunity to do it early and with real ownership.