Vice President Data Science Jobs in Wisconsin (NOW HIRING)

Remote Position - must reside in Florida, Texas, Illinois, New York, New Jersey, Colorado, Idaho, Massachusetts, Michigan, Minnesota, Missouri, North Carolina, South Carolina, Utah or Virginia

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.

Are you ready to make yourself at home?

We're seeking a hands‑on VP, Enterprise Data & Analytics to own and lead TradeStation's data science, analytics, and data product functions end to end — and to make data TradeStation's competitive advantage. Reporting directly to the SVP of Product Strategy, this executive owns the multi‑year strategy, the operating model, and the business outcomes of the entire data domain: activating the company for data‑driven decisions, building data products, and laying the data foundation that fuels AI. This is a working leadership role — the VP sets the vision and strategy and stays hands‑on in the data, the platform, and the code, not just managing from above.

This role owns the disciplines that turn data into value:

• Data Science & Advanced Analytics – Own the data science and analytics function, delivering predictive modeling, actionable insights, and advanced analytics that inform product, operational, and business strategy.
• Data Products & Data‑Driven Activation – Own the data product portfolio and Data Product Management function; activate the company for data‑driven decisions and treat data as a product and a durable competitive advantage.
• Data Foundations for AI – Lay out the vision and build the structure that makes data the fuel for AI: the trusted, governed data foundations, semantic layer, and pipelines that AI agents and agentic brokerage workflows operate from.

Building on that foundation, the VP partners with the Enterprise Analytics engineering team to design the enterprise data foundation and pipelines — which Engineering builds and maintains — with prioritization flowing through the Data Product Management function this VP owns. The VP will build and lead a multi‑tier organization, directly managing Director- and Senior Director‑level leaders across Data Science, Data Products, and Enterprise Data, and is expected to represent the data function at the executive leadership level.

What You'll Be Doing:

Set the Vision & Own the Outcomes

• Deliver, not just direct – stay hands‑on and personally ship the hardest, highest‑leverage data science, analytics, and data‑product work, setting the pace and the technical bar by delivering rather than overseeing
• Own the data science, analytics, and data product vision, strategy, and multi‑year roadmap – set direction and investment priorities for the entire data domain and connect them to TradeStation's business strategy
• Be accountable for domain‑level outcomes – own the OKRs and business results of the data function as a whole: data‑driven decisions activated, adoption, quality, trust, insight, and measurable business impact
• Make data TradeStation's competitive advantage – define how proprietary and market data is turned into differentiated products, insights, and decisions the competition can't easily replicate
• Define the operating model and standards – establish the prioritization frameworks, delivery standards, and ways of working that let the data organization scale and run predictably across teams

Data Science & Advanced Analytics (Owned by This Role)

• Build and lead the firm's data science and advanced analytics organization, delivering predictive modeling, trader‑behavior insights, and business‑impact analysis that directly inform product and business strategy
• Stay hands‑on in the analytics – personally shape models, metrics, and analyses, writing and reviewing SQL and Python to prove out solutions and set the technical bar by example
• Establish frameworks for scalable analytics delivery, ensuring insights reliably influence product strategy, operational efficiency, and customer engagement
• Evangelize and activate a data‑driven culture across TradeStation, enabling teams with training, best practices, and self‑service data and analytics tooling so decisions are made on trusted data
• Own the data product portfolio and the Data Product Management function – set requirements, priorities, and roadmaps, and lead the shift from bespoke, project‑based reporting to standardized, governed, reusable data products with clear owners and single sources of truth.
• Drive prioritization into Engineering – funnel enterprise data foundation and pipeline priorities through Data Product Management so the Enterprise Analytics engineering team is building the highest‑value work
• Own the semantic layer strategy – define the business‑friendly metrics, definitions, and data models that ensure a single source of truth across analytics and reporting
• Drive the value of data products – identify opportunities to turn proprietary and market data into high‑value internal solutions and, where applicable, externally facing or monetizable data products; measure and report their business value
• Own third‑party and market data sourcing – evaluate, integrate, and manage relationships and licensing for external and market data sources that power TradeStation's data products
• Set the architecture and direction for the enterprise data foundation – cloud data platform (AWS, with Snowflake and/or Databricks), lakehouse and medallion architectures, dimensional and semantic data modeling, and batch + streaming pipelines (Spark, Airflow, dbt) – building on the foundation established at the Senior Director level
• Direct real‑time and low‑latency data capabilities for trading and market data – streaming ingestion and processing (Kafka / Amazon MSK, Flink) of high‑volume market data feeds to power analytics, products, and operations
• Partner with the Enterprise Analytics engineering team, who build and maintain the pipelines and foundation – define what gets built, set standards, and provide hands‑on technical direction, while Engineering owns ongoing build and maintenance
• Ensure the foundation is trusted, compliant, and scalable so data flows reliably into analytics, products, and AI workflows across TradeStation

Data Foundations for AI & Agentic Workflows

• Lay out the vision and strategy for how data fuels AI – partner with the AI Product & Solutions team and deliver enablement through data, building the data structure, standards, and foundations that team operates from
• Design the trusted, governed data foundations, semantic layer, and pipelines that AI agents and agentic brokerage workflows depend on – making enterprise data ready to power automated, AI‑driven decisions and operations
• Stand up the GenAI data layer – retrieval‑augmented generation (RAG) pipelines, embeddings and vector databases, feature stores, and MLOps practices that let models and agents operate on trusted, governed enterprise data
• Partner with the AI Product & Solutions team as the supplier of the data fuel – align on requirements, access, quality, and governance so AI initiatives can move fast on a solid data foundation
• Leverage AI to accelerate the data function itself – operationalize AI/LLM tools to automate data profiling, anomaly detection, documentation, and analytics workflows at scale

Hands‑On Technical Leadership

• Operate as a working leader, not just a manager – write and review production‑grade SQL and Python, prototype data products and models, and validate solutions personally rather than delegating all execution
• Set technical standards by example – establish patterns, review approaches, and roll up your sleeves on the hardest data problems (governance at scale, quality and trust, platform and semantic‑layer design)
• Stay close enough to the data and tooling to make credible architecture and prioritization calls and to partner as a peer with Engineering and Data Science

Data Governance, Quality & Compliance

• Establish and own enterprise data governance – design and institutionalize frameworks for data classification, lineage, access controls, metadata management, and master data management (MDM) as company‑wide policy, operationalized through data catalog and governance tooling (e.g., Collibra, Alation)
• Drive enterprise data quality and trust – define monitoring, remediation strategies, and quality KPIs using automated data‑quality and observability tooling (e.g., Great Expectations, Monte Carlo); hold teams across the organization accountable to trusted, reliable data
• Own regulatory, privacy, and compliance alignment – ensure enterprise data practices meet SEC/FINRA and financial‑services requirements and data‑privacy regulations (e.g., CCPA, GDPR), with proper PII handling and internal security policies; partner with Compliance, Risk, and InfoSec and support regulatory examinations and audits

Organizational Leadership & Executive Influence

• Serve as the executive‑facing authority on data and analytics – secure buy‑in and decisions at the executive leadership level, representing the function in senior leadership forums, executive reviews, and enterprise risk committees
• Build and develop the organization – hire, manage, and mentor Director‑ and Senior Director‑level leaders across Data Science, Data Products, and Enterprise Data; raise the bar for data practices company‑wide
• Influence and align across the organization – build consensus and drive decisions across Product, Engineering, Data Science, Enterprise Analytics, and business teams
• Deliver quantifiable business outcomes – efficiency gains, increased trading activity, customer growth, insight‑driven decisions, and risk reduction – tracked rigorously against clear KPIs

• Domain Ownership & Executive Leadership – proven ability to own an entire data and analytics domain, set multi‑year strategy, manage Director‑level leaders, and be accountable for organization‑level business outcomes
• Hands‑On SQL & Python – personally fluent and current in advanced SQL (complex queries, performance tuning, data modeling) and Python (data manipulation, scripting, automation); able to prototype data products and models, write and review production‑grade code, and set technical standards by doing the work
• Modern Data Stack & Cloud – deep hands‑on experience with cloud data platforms (AWS primary; Azure or GCP a plus), cloud warehouses/lakehouses (Snowflake, Databricks), transformation (dbt), orchestration (Airflow), distributed processing (Spark), and streaming (Kafka / Amazon MSK, Flink)
• Data Architecture & Modeling – modern architectures (lakehouse, medallion, data mesh), dimensional and semantic data modeling, and design of both batch and real‑time/streaming pipelines at enterprise scale
• Semantic Layer & Metrics Design – experience designing and implementing semantic/metrics layers (e.g., dbt Semantic Layer, Cube, AtScale, LookML) and translating business concepts into consistent, reusable, governed definitions and single sources of truth
• Data Science & Machine Learning – experience leading teams delivering statistical modeling, machine learning, and predictive analytics; familiarity with the ML lifecycle and MLOps (model deployment, monitoring, feature stores)
• AI & GenAI for Data – hands‑on understanding of LLMs, retrieval‑augmented generation (RAG), embeddings and vector databases, and agentic workflows; able to architect the trusted, governed data foundations that AI products and agents operate on, partnering with and enabling AI product teams through data
• Enterprise Data Governance – proven track record institutionalizing governance, data quality and observability, metadata management, lineage, and master data management (MDM) using tooling such as Collibra or Alation, across complex, regulated organizations
• BI & Data Visualization – fluency with enterprise BI/visualization platforms (Tableau, Power BI, Looker, Sigma) and enablement of self‑service analytics across the company
• Strategic Data Leadership – track record driving organizational change (e.g., productizing data) and standing up enterprise data and analytics capabilities from vision through execution
• Cross‑Functional Delivery with Engineering – proven ability to partner with engineering teams that build and maintain pipelines, driving priorities through a data product management function while providing technical direction
• Executive Presence & Influence – proven ability to influence and advise at the executive leadership level, secure buy‑in and decisions from senior executives, and align cross‑functional organizations with influence rather than authority
• Outcome‑Driven Operator – defines clear KPIs across data and analytics initiatives, tracks progress rigorously, and holds teams accountable for measurable busi