About CrazyCatz

CrazyCatz was incorporated to exploit the very latest developments in Artificial Intelligence to realize the vision of "Agents where people don't need to be". Specifically, despite the founders extensive experience in building multiple waves of market defining innovations in IT and SecOps, there is still a wasteful use of human resources performing repetitive and low impact knowledge work. These people need liberating to free them from drudgery and CrazyCatz will pioneer this change.

We will build a series of unique foundational models, some based upon learning the patterns and sequences in machine data, to drive a leading edge agent first platform. Replacing SIEM/SOAR and XDR by the revolutionary combination of AIDR (AI Detection & Response) for SecOps and a novel Generative AIOps platform, we will bring to market the world's first XOps platform. Humans will only ever be pulled into the loop to oversee the army of agents that will secure and assure the most sensitive and critical digital infrastructures. This represents a $50 billion market worldwide, and the opportunity with a unique US & UAE capital strategy, to build the fifth essential AI property.

We are seeking talented experts keen to join the next wave of AI first engineering and help us build a future genuinely free of toil.

Position Summary

The Data Scientist is a hands-on technical contributor responsible for turning large volumes of machine-generated data (security telemetry, operational logs, and infrastructure signals) into the modeling foundations that power CrazyCatz's AIDR and Generative AIOps platform. Reporting to the Chief Scientist, this role sits at the center of the company's foundational model strategy: exploring data, engineering features, building and validating models, and partnering closely with research and engineering to move findings from notebook to production.

This role is ideal for a data scientist who is equally comfortable in exploratory analysis and rigorous statistical modeling, and who wants to apply those skills to sequence learning, anomaly detection, and pattern recognition in high-volume machine data rather than conventional business analytics.

The successful candidate will play a direct role in shaping the datasets, features, and modeling approaches that underpin CrazyCatz's proprietary foundation models.

Key Responsibilities

Data Exploration & Modeling

• Analyze large-scale machine-generated datasets (security events, logs, telemetry, sensor and system data) to identify patterns, anomalies, and signal relevant to detection and operations use cases.

• Design, build, and validate statistical and machine learning models for anomaly detection, sequence learning, classification, and forecasting.

• Translate ambiguous business and product questions into well-defined data science problems.

Feature Engineering & Data Pipelines

• Develop robust feature engineering pipelines for high-volume, high-velocity machine data.

• Partner with data engineering to ensure data quality, lineage, and reliability across training and evaluation datasets.

• Establish reusable data preparation standards that scale across multiple model initiatives.

Model Evaluation & Rigor

• Define and apply evaluation methodologies to measure model accuracy, robustness, false-positive/false-negative rates, and real-world effectiveness.

• Conduct error analysis and iterate on models based on production and customer feedback.

• Document experiments, assumptions, and results to a standard suitable for scientific and engineering review.

Collaboration & Productionization

• Work closely with the Chief Scientist and research team to align data science work with the company's foundational model roadmap.

• Partner with engineering to productionize models within the AIDR and Generative AIOps platform.

• Communicate findings clearly to technical and non-technical stakeholders, including product and executive teams.

Continuous Learning

• Stay current with advances in machine learning, sequence modeling, anomaly detection, and applied AI relevant to security and operations data.

• Bring new techniques and tooling into the team's practice where they offer a meaningful advantage.

Qualifications

Required Experience

• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field.

• 5+ years of experience as a data scientist or applied machine learning practitioner, ideally working with large-scale or high-velocity datasets.

• Strong foundation in statistics, machine learning, and experimental design.

• Proficiency in Python and standard data science tooling (e.g., pandas, scikit-learn, PyTorch or TensorFlow).

• Demonstrated experience taking models from exploratory analysis through to validated, production-ready outputs.

Preferred Experience

• Experience working with security, observability, IT operations, or other machine-generated/log-style data.

• Familiarity with anomaly detection, time-series or sequence modeling, and unsupervised learning techniques.

• Exposure to foundational or large language models, and an interest in applying them to non-traditional data domains.

• Prior startup or scale-up experience, particularly in cybersecurity or AIOps.

• Advanced degree (Master's or PhD) in a relevant quantitative discipline.

Competencies

• Analytical Rigor

• Curiosity & Problem Solving

• Technical Craftsmanship

• Clear Communication

• Collaboration Across Disciplines

• Ownership & Follow-Through

• Adaptability in an Ambiguous, Fast-Moving Environment

Success Measures

During the first 12 months, the Data Scientist will:

• Deliver validated models or analyses that directly inform CrazyCatz's foundational model strategy.

• Establish repeatable data preparation and evaluation standards for machine-data modeling work.

• Contribute measurable improvements to detection accuracy or operational insight within the AIDR or Generative AIOps platform.

• Build strong working relationships with the research and engineering teams to move work efficiently from analysis to production.

• Be recognized internally as a trusted, hands-on technical contributor to the company's scientific roadmap.

Reporting Relationship

Reports to: Chief Scientist

Location: Remote / Hybrid

Compensation: Competitive salary, bonus, and equity participation.