Principal Data Scientist

We're seeking a Principal Data Scientist to help design, build, and deploy advanced analytics and machine learning solutions that power TradeStation's trading platform, client experience, and business operations. Reporting to the Sr. Director of AI, Data Science & Enterprise Data, this role will own the full data science lifecycle - from exploratory analysis and model development through production deployment and ongoing monitoring.

This role is for a self-directed practitioner who thrives without hand holding. The ideal applicant will proactively identify opportunities where data science can create meaningful impact, build the solutions, and iterate rapidly. The Principal Data Scientist will write clean, production-grade code, stay genuinely curious about emerging AI and ML techniques, and independently learn new tools before being asked to. The applicant should be equally at home in a Jupyter notebook and a production ML pipeline.

This role will work closely with Product, Engineering, Compliance, and Analytics teams to build predictive models, behavioral analytics, and AI-powered capabilities that give TradeStation a competitive edge.

What You'll Be Doing:

Modeling & Machine Learning

• 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

Analytics & Visualization

• 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

AI Integration & Innovation

• 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

Strategic Impact & Communication

• 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

• 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
• 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, * 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

• 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
• Pay Range (US) $140-180K (Countries outside of the US have differing ranges in accordance with local labor markets)

TradeStation is the home of those born to trade. As an online brokerage firm and trading ecosystem, we are focused on delivering the ultimate trading experience for active traders and institutions. We continuously push the boundaries of what's possible, encourage out-of-the-box thinking, and relentlessly search for like-minded innovators. At TradeStation, we are building an AI-First culture. We expect team members to embrace AI as a core part of their daily workflow, whether that's using AI to accelerate development, enhance decision-making, improve client outcomes, or streamline internal processes. We hire, grow, and promote people who can harness AI responsibly and creatively. We treat AI as a partner in problem-solving, not just a tool; following our governance standards to ensure AI is used ethically, securely, and transparently. If you join us, you're joining a culture where AI is how we work.