Senior Data Scientist, Applied ML

SpyCloud•Austin, TX

20h•Hybrid

About The Position

SpyCloud is looking for 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 deployment in production. The position requires close collaboration with engineering, product, and research teams to transform complex problems into scalable, reliable systems. This role is suited for individuals who excel in applied, hands-on environments where impact and collaboration are paramount, and who have end-to-end ownership of data-related 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 the handoff points between research and production.
Work closely 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 and rapidly prototype MVPs.
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.

Benefits

401(k) with Employer Contribution
Health, Vision, and Dental Insurance
Health Savings Account (HSA) available with Employer Contribution
Employer Paid Life, Short-term, and Long-term Disability Insurance
Generous PTO Plan and 16 paid holidays per year
Retirement Savings Plan with Employer Contribution (U.K.)
Employer Provided Private Health Insurance and Healthcare Cashplan (U.K.)
Employer Paid Life Insurance, Income Replacement, and Critical Illness Protection (U.K.)
Employee Assistance Program (U.K.)
25 days annual accrued holiday plus 8 paid bank holidays and a paid company shutdown during winter holidays (U.K.)
Engaging workspace in South Austin
Flexible and remote-friendly work options