2 months ago
Responsibilities
- Design, build, tune, validate, and maintain machine-learning models for security anomaly detection and risk scoring.
- Operationalize models and detections in production with enrichment, correlation, response hooks, versioning, rollback, and detection-as-code practices.
- Engineer features from identity, endpoint, network, cloud, SaaS, and application telemetry in security data lakes and streaming pipelines.
- Partner with the SOC to triage alerts, incorporate analyst dispositions as labels, reduce noise, and maintain runbooks.
- Translate threat hypotheses and security or fraud scenarios into model-backed analytics with measurable success metrics.
- Establish model governance through evaluation, drift and data-quality monitoring, retraining, re-baselining, explainability, traceability, and privacy controls.
- Participate in incident reviews, identify coverage gaps, and deliver resulting models and detections.
- Contribute to security ML platform architectures, standards, and documentation, and mentor engineers and analysts.
Requirements
- 7+ years of hands-on experience building and operating production machine-learning models for detection or anomaly detection across supervised and unsupervised approaches.
- Experience with data lakes and big-data technologies such as Snowflake, Databricks, Spark, Delta or Iceberg, and S3 or GCS.
- Strong Python and SQL skills with hands-on use of pandas, scikit-learn, PyTorch, or TensorFlow.
- Understanding of identity, endpoint, network, cloud, and SaaS security telemetry and how to convert it into model features.
- Working knowledge of statistical baselining, clustering, isolation forests, autoencoders, time-series methods, and the end-to-end model lifecycle.
- Familiarity with MITRE ATT&CK, the kill chain, and adversary tradecraft.
- Experience collaborating with SOC/DFIR and fraud or risk teams, with strong written communication skills.
- Bachelor’s degree in computer science, data science, statistics, or a related field, or equivalent practical experience.
- Preferred experience includes streaming data engineering, AWS machine-learning deployment, MLOps, graph-based analytics, deep learning or LLM-based security applications, and relevant cloud, data engineering, or Databricks certifications.
Benefits
- Comprehensive benefits are available through SoFi’s Benefits page.
- Remote work cannot be accommodated from Hawaii or Alaska due to insurance coverage issues.
Tech Stack
Apache FlinkApache KafkaApache SparkAWSAzureDatabricksGoogle CloudGoogle Cloud PlatformPandasPythonPyTorchscikit-learnSnowflakeSQLTensorFlow
Categories
About SoFi
SoFi provides consumer banking, lending, and investing products, including student-loan refinancing, personal loans, mortgages, checking and savings, a credit card, and brokerage. It earns interest and fee revenue across SoFi Bank, N.A., and related subsidiaries, serving U.S. consumers via a mobile app. Founded in 2011 and headquartered in San Francisco, SoFi Technologies is publicly traded on Nasdaq under the ticker SOFI.