Billy Goat Group

Full-stack Engineer

• Lead full stack architecture and system design
• Take point on initiatives
• Own end-to-end execution across frontend and backend
• Optimize existing systems for performance
• Serve as the team's technical anchor
• Manage technical roadmap
• Make technical recommendations
• Coordinate and communicate seamlessly across teams
• Leverage AI coding tools to accelerate development

Senior Full Stack Engineer

Machine Learning Engineer

• Own the full lifecycle of predictive models in production — architecture, training pipelines, inference infrastructure, deployment, and ongoing model health
• Build and operate the systems that route model outputs into live product surfaces: search ranking, recommendations, feed ordering, and related user-facing experiences
• Establish and maintain model monitoring, alerting, drift detection, and retraining cadences — the feedback loops that keep deployed models accurate over time
• Partner closely with Data Science, Data Engineering, Product Management, and backend engineering to move work from validated approach to production system
• Own the decision-making process on whether to leverage ML infrastructure & expertise from our parent company, GOAT Group, and when to advocate for building in-house solutions.
• Contribute to ML infrastructure decisions — serving architecture, feature computation, pipeline orchestration — with an eye toward what scales as the team and model count grows
• Set technical standards and raise the bar for how ML systems are built, evaluated, and operated across the pod

Staff Machine Learning Engineer

Data Engineer

• Own the reliability, availability, and accuracy of our data infrastructure
• Build, maintain, and improve our data pipeline using our modern data stack
• Build centralized, durable, and reusable data models
• Build and maintain a semantic layer with canonical dimension and metric definitions
• Partner closely with Analysts, PMs, Engineers, Marketing, Legal, Fraud, and other stakeholders
• Build Reverse ETL pipelines in Python
• Proactively bring in new data sources
• Champion data governance and help elevate the organization's data maturity

Senior Data Engineer

• Lead backend architecture and system design, proactively proposing and driving new systems that support critical customer facing features
• Take point on initiatives, owning the backend roadmap, driving technical decisions, and coordinating cross-functional delivery
• Optimize existing systems for performance, reliability, and cost. You treat production health as a first-class priority, not an afterthought
• Serve as the team's technical anchor: set engineering standards, lead design reviews, unblock teammates
• Manage technical roadmap and problems of significantly larger scope, from formulating an efficient game plan to delivering stable, well-tested features on time
• Make technical recommendations that improve the codebase while minimizing risk; identify and fix (or ideally prevent) bugs and performance bottlenecks
• Coordinate and communicate seamlessly across engineering, product, and design teams
• Leverage AI coding tools (Claude, Codex, etc.) to accelerate development velocity for yourself and the team

Senior Software Engineer

Risk

• Own the fraud rule lifecycle — designing, testing, deploying, and continuously optimizing detection rules and thresholds to maximize catch rate while minimizing friction.
• Lead AI-enabled fraud use cases end-to-end: use-case sizing, feature ideation, population analysis, and data quality assessment to ensure solutions are analytically sound and value-accretive.
• Conduct periodic performance reviews of fraud models and rules; drive root-cause investigations to proactively surface risk before it scales.
• Serve as a trusted advisor for AI and ML use cases within fraud, ensuring alignment with enterprise AI governance standards, regulatory expectations, and the AI Use Case Registry.
• Evaluate vendor fraud solutions through an analytical lens — assessing effectiveness, overlap, and value relative to cost and risk-reduction objectives.
• Stay current on AI and automation tools and actively explore how they can enhance fraud detection workflows and efficiency.
• Write and maintain intermediate-to-advanced SQL queries to investigate fraud signals, identify connected accounts and orders, and surface emerging patterns.
• Develop and maintain executive dashboards covering fraud review rates, chargeback performance, model effectiveness, and loss trends.
• Translate complex analytical findings into clear, executive-ready narratives — articulating implications for fraud loss, operational efficiency, and customer experience.
• Proactively identify trends and anomalies at both the individual order and systemic level, communicating the full arc of a problem — from discovery to resolution — to any audience.
• Support daily fraud review operations — account verification, order review, escalation handling, SLA adherence, and QA.
• Serve as an internal escalation point for complex cases; assist in coaching the team and continuously improve review processes.
• Partner cross-functionally with Product, Engineering, Compliance, and Legal to align fraud controls with regulatory requirements and product priorities.
• Manage law enforcement requests as needed.

Senior Manager, Fraud Analytics, Risk

Analyst

• Use SQL extensively to analyze marketplace performance data and uncover actionable insights across key metrics (liquidity, active quantity, pricing, engagement, and sell-through) surfacing trends, opportunities, and risks to stakeholders
• Analyze sales, stock on-hand, and on-order at the style/color/size level to make recommendations on style selections, unit allocations, and pricing strategies that balance revenue opportunity with profitability
• Perform exploratory / “ad-hoc” data analysis to support various global strategic initiatives
• Stay on top of product trends across categories and conduct competitive intelligence research to identify emerging areas and maintain a best-in-class assortment
• Monitor active supply metrics to ensure marketplace supply meets future demand and track initiatives like SKU-based incentives to measure opportunity recapture
• Develop and maintain SQL-based dashboards that support the Business Development team
• Evaluate current reporting and seek out new ways to enhance how performance is tracked
• Support projects and tasks from manager and other key stakeholders on an as-needed basis
• Develop and optimize workflows using templates and AI tools across systems

Senior Full Stack Engineer

Staff Machine Learning Engineer

Senior Data Engineer

Senior Software Engineer

Senior Manager, Fraud Analytics, Risk

Supply Analyst