Principal Data Scientist
$140,000–$180,000 year
Remote · New York, United States or Texas, United States
Job Summary
Principal Data Scientist at TradeStation (Remote; US states listed) leads end-to-end ML initiatives for trading platform analytics, user behavior insights, fraud detection, and risk modeling. Owns problem framing, feature engineering, model training, deployment, and monitoring; builds real-time and batch ML pipelines; applies NLP/LLM techniques to unstructured data; translates complex results into clear dashboards and executive narratives; collaborates with Product, Engineering, Compliance, and Analytics to deliver predictive capabilities and governance-aligned ML solutions. Requires production-grade Python, MLOps experience, cloud ML infrastructure, Databricks/Spark/Snowflake proficiency, strong statistical rigor, and ability to operate independently across cross-functional teams.
Required Qualifications
- Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field
- 7+ years of experience in data science or applied machine learning roles, with demonstrated ownership of models deployed to production
Desired Qualifications
- 7+ years of experience in data science or applied machine learning with demonstrated ownership of models deployed to production
- Master's or PhD in a quantitative discipline (preferred)
- Experience building and monitoring ML models in production using MLOps platforms (e.g., MLflow, SageMaker, Vertex AI)
- Hands-on experience with LLM APIs, RAG architectures, or AI agent frameworks (preferred)
- Experience with fraud detection, behavioral anomaly detection, trade surveillance, or risk modeling in financial services (preferred)
- Familiarity with real-time streaming data (Kafka, Spark Streaming) and low-latency model serving (preferred)
- Experience with cloud ML infrastructure (Azure, AWS, or GCP) and distributed computing (preferred)
- Strong software engineering fundamentals (production-quality Python, Git, Docker)
- Data platform proficiency (Databricks, Spark, Snowflake; SQL)
- Visualization and storytelling skills (Tableau, Power BI, Plotly, Sigma)
- Ability to translate business questions into analytical solutions and present to executives
- Independent learner with a track record of self-directed projects or contributions
- Cross-functional collaboration with Product, Engineering, Compliance, and Analytics teams
- Understanding of AI governance, interpretability, and responsible deployment
- Excellent communication skills and ability to work with non-technical stakeholders
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