Senior Data Scientist, Applied ML

SpyCloud•Austin, TX

1d•$154,000 - $200,000•Hybrid

About The Position

SpyCloud is seeking a Senior Data Scientist, Applied ML to design, build, and deploy models for critical cybersecurity use cases such as incident detection and mitigation, fraud intelligence, and risk scoring. This role involves owning the full model lifecycle, from data understanding and preparation through prototyping and production deployment. The position requires close collaboration with engineering, product, and research teams to transform complex problems into scalable, reliable systems. It is ideal for individuals who excel in applied, hands-on environments where impact and collaboration are paramount, and who have end-to-end ownership of data work.

Requirements

4+ years of experience building and shipping models in production with direct, hands-on ownership of the data lifecycle around them
Strong background in applied math (linear algebra, optimization, statistics) and machine learning
Demonstrated experience leveraging Natural Language Processing (NLP) techniques for text classification, tagging, or entity extraction
Proficiency in Python and key ML libraries: PyTorch, TensorFlow, scikit-learn, XGBoost
Demonstrated experience building or maintaining data/feature pipelines (e.g., with Airflow, Spark, Pandas) as part of your own modeling work
Comfort with model versioning and monitoring in production (e.g., MLflow, DVC)
Working experience deploying models into cloud environments or containerized services
Strong communication skills and the ability to translate complex problems into actionable solutions

Nice To Haves

Deeper MLOps/DevOps/data engineering exposure: infra-as-code, CI/CD depth, etc.
Familiarity with cybersecurity datasets or domains: threat intelligence, account takeover, ransomware, etc.
Exposure to graph analytics, knowledge graphs, or cybersecurity frameworks like MITRE ATT&CK
Background working with unstructured data (e.g., log files, threat reports, breach datasets)

Responsibilities

Develop, train, and deploy models using real-world structured and unstructured data to power critical security features such as threat detection and alerting, entity resolution and risk scoring, and natural language-based tagging and classification.
Build preprocessing and feature engineering pipelines for models.
Own model monitoring and evaluation, designing feedback loops to continuously improve accuracy and effectiveness.
Prototype new approaches from scratch and take existing prototypes to production-grade reliability.
Take ownership of data validation, transformation, and pipeline health across research and production handoff points.
Collaborate with software and data engineers to productionize models in cloud-native environments like AWS.
Partner with product managers and domain experts to define success criteria.
Rapidly prototype MVPs to test new features or signals.
Work with the data engineering team to access and understand diverse data sources, owning the transformation and validation steps.
Contribute to broader system design and architectural decisions.
Clearly articulate model design choices, tradeoffs, and outcomes to both technical and non-technical stakeholders.
Maintain thorough documentation for models, pipelines, and evaluation methodologies.
Participate in model and compliance reviews and customer-facing discussions as needed.