Senior Data Scientist (Applied Machine Learning)

• We’re looking for a Senior Data Scientist, Applied ML to design, build, and deploy models for critical cybersecurity use cases like incident detection and mitigation, fraud intelligence, and risk scoring
• You’ll own the full model lifecycle — from data understanding and preparation through prototyping and deployment in production — and work closely with engineering, product, and research teams to turn complex problems into scalable, reliable systems
• This role is ideal for someone who thrives in applied, hands-on environments where impact and collaboration matter, and who has genuinely owned data work end-to-end
• You will 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
• You’ll build the preprocessing and feature engineering pipelines your own models depend on, and you’ll own model monitoring and evaluation, designing feedback loops to continuously improve accuracy and effectiveness
• You’ll be equally comfortable prototyping new approaches from scratch and taking existing prototypes — from our R&D team or your own experimentation — to production-grade reliability
• This role sits deliberately at the intersection of research and deployment, not on one side of it: you’ll take ownership of data validation, transformation, and pipeline health across the handoff points between research and production, not just within the boundaries of your own models
• Working closely with software and data engineers, you’ll help productionize models in modern cloud-native environments like AWS
• This role is highly collaborative. You’ll partner with product managers and domain experts to define success criteria, rapidly prototype MVPs to test new features or signals, and work with the data engineering team to access and understand diverse data sources, owning the transformation and validation steps throughout
• Your input will also contribute to broader system design and architectural decisions
• Strong communication and documentation skills are essential. You will clearly articulate model design choices, tradeoffs, and outcomes to both technical and non-technical stakeholders, maintain thorough documentation for models, pipelines, and evaluation methodologies, and participate in model and compliance reviews and customer-facing discussions as needed

Benefits

• Health benefits: From medical to dental, we’ve got you covered.
• Work/life balance: We offer generous PTO and a remote-friendly culture.
• Compensation: Our talented employees enjoy competitive salaries and equity.
• 401k matching: Investing in the future of our employees is a no-brainer.
• SpyCares: We’re only as strong as the communities we’re a part of and giving back is in our DNA.- Comfort with model versioning and monitoring in production (e.g., MLflow, DVC)
• Demonstrated experience building or maintaining data/feature pipelines (e.g., with Airflow, Spark, Pandas) as part of your own modeling work
• 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
• Strong background in applied math (linear algebra, optimization, statistics) and machine learning
• Strong communication skills and the ability to translate complex problems into actionable solutions
• Working experience deploying models into cloud environments or containerized services
• 4+ years of experience building and shipping models in production with direct, hands-on ownership of the data lifecycle around them
• 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)