[Remote] Principal Data Scientist

Note: The job is a remote job and is open to candidates in USA. TradeStation is an online brokerage firm focused on delivering the ultimate trading experience for active traders and institutions. They are seeking a Principal Data Scientist to design, build, and deploy advanced analytics and machine learning solutions that enhance their trading platform and client experience.

Responsibilities

• Own the end-to-end ML lifecycle — from problem framing and feature engineering through model training, validation, deployment, and ongoing performance monitoring
• Help build and deploy predictive models across a range of use cases including customer behavior, fraud and anomaly detection, trade surveillance, risk modeling, and personalization
• Design and implement real-time and batch ML pipelines that operate reliably at scale in production environments
• Develop behavioral anomaly detection and pattern recognition systems using statistical and deep learning approaches
• Apply NLP and LLM techniques to extract insights from unstructured data — trade notes, client communications, market commentary, and internal documentation
• Translate complex data into clear, compelling visualizations and narratives for both technical and executive audiences
• Help design and build dashboards and analytical tools that empower stakeholders to make faster, more informed decisions
• Conduct exploratory data analysis to surface trends, anomalies, and opportunities across trading behavior, customer segments, and platform performance
• Define, track, and interpret key business and model performance metrics; proactively surface meaningful insights without waiting to be asked
• Stay at the forefront of AI and ML research — continuously evaluate and adopt emerging techniques (GenAI, RAG, agents, multimodal models) where they create real business value
• Leverage AI tools (Claude, LLMs, foundation models) to accelerate your own development workflow, from code generation to documentation to data profiling
• Experiment rapidly with new approaches; fail fast, iterate, and bring winning solutions to production
• Contribute to TradeStation's AI governance standards by ensuring models are interpretable, fair, and deployed responsibly
• Partner with Product and Engineering to define the data and modeling requirements for new platform features
• Work with Compliance and Risk teams to build surveillance and monitoring systems that meet regulatory requirements
• Communicate results and recommendations clearly to non-technical stakeholders; translate business questions into rigorous analytical frameworks

Skills

• 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
• Self-Starter & Independent Learner — proactively identify problems worth solving, learn new techniques without being prompted, and drive projects to completion without needing direction
• Full-Stack Data Science — proven ability to own the complete lifecycle: problem definition, data wrangling, feature engineering, modeling, deployment, and monitoring in production environments
• Machine Learning Depth — strong command of supervised, unsupervised, and reinforcement learning methods; experience with time series, anomaly detection, NLP, and deep learning; know when to use simple models and when to go complex
• Software Engineering Fundamentals — writes production-quality Python; comfortable with version control (Git), containerization (Docker), and MLOps best practices; code that others can maintain
• Data Platform Proficiency — hands-on experience with Databricks, Spark, or Snowflake; able to write and optimize complex SQL; understands data modeling and pipeline design
• Visualization & Storytelling — ability to build polished, insight-driven visualizations and dashboards (Tableau, Power BI, Plotly, Sigma); presents data science work in business terms
• AI-Native Workflow — actively uses AI tools (Claude, Copilot, LLMs) in day-to-day work; has hands-on experience with LLM APIs, prompt engineering, or GenAI application development
• Statistical Rigor — solid grounding in probability, statistics, and experimental design; applies A/B testing and causal inference correctly; doesn't overfit spurious signals
• Cross-Functional Collaboration — comfortable working across Product, Engineering, Compliance, and Analytics; can present findings to executives and translate business requirements into analytical solutions
• Master's or PhD in a quantitative discipline
• Financial Services Domain— experience with trading data, market microstructure, customer behavior in financial platforms, fraud detection, or regulatory compliance analytics strongly preferred
• Experience building and monitoring ML models in production using MLflow, SageMaker, Vertex AI, or similar MLOps platforms preferred
• Hands-on experience with LLM APIs, RAG architectures, or AI agent frameworks preferred
• Track record of self-directed learning — personal projects, open-source contributions, Kaggle competition history, technical writing, or conference presentations 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

Benefits

• Collaborative work environment
• Competitive Salaries
• Yearly bonus
• Comprehensive benefits for you and your family starting Day 1
• Unlimited Paid Time Off
• Flexible working environment
• TradeStation Account employee benefits, as well as full access to trading education materials

Company Overview

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