Data Labelling Jobs in Miami, FL (NOW HIRING)

Octagon Talent Solutions is partnering with a fast-moving financial technology company that is building advanced machine learning products to detect fraud, strengthen identity verification, and support better real-time risk decisioning across financial services.

We are seeking a Head of Data Science to lead a growing team of full-stack data scientists responsible for developing production-grade models that identify fraudsters and expand the company’s suite of financial risk products. This is a high-impact leadership role for someone who combines strong applied machine learning expertise, deep business intuition, and the ability to mentor talented data scientists through complex, high-visibility work.

In this role, you will directly manage a team that starts at approximately 2–3 data scientists and grows to 5–6. You will serve as a technical leader, mentor, and domain owner across application fraud, helping the team build models and analytical systems that influence real-time decisions for partners. The right candidate will be energized by end-to-end ownership, rapid iteration, and the kind of deep domain understanding that creates durable competitive advantage.

Responsibilities

• Lead, mentor, and directly manage a team of highly skilled full-stack data scientists focused on application fraud, financial risk, and identity verification products.
• Provide hands-on technical direction across model development, analysis, experimentation, production code, monitoring, and fraud-focused decision systems.
• Guide the team through the full machine learning model development lifecycle, including data acquisition decisions, labeling strategy, featurization, model training, experimentation, productionalization, and ongoing performance monitoring.
• Partner closely with senior leadership, product, engineering, risk operations, marketing, and sales teams to align priorities, communicate progress, and deliver high-impact solutions on aggressive timelines.
• Develop strong business intuition around fraud patterns, risk signals, user behavior, and partner needs, then translate that understanding into practical data science solutions.
• Research emerging fraud behaviors and help create new products and capabilities around identity verification and application risk.
• Drive success through rapid iteration, integration of new data sources, inventive feature engineering, and disciplined evaluation of model performance.
• Write and review production-ready code used in real-time decision-making systems.
• Design, perform, and present analyses that inform data acquisition, product development, risk operations priorities, marketing strategy, and sales efforts.
• Challenge the team’s thinking, probe assumptions, and create an environment where data scientists consistently produce their best work.

Requirements

• 7–15 years of experience in applied machine learning, data science, or a closely related technical field.
• Proven experience building and deploying production machine learning models in fintech, cybersecurity, fraud detection, identity verification, risk, trust and safety, or another high-stakes domain.
• Experience managing or mentoring high-performing data scientists, machine learning engineers, or analytically rigorous technical teams.
• Strong hands-on technical ability across model development, statistical analysis, feature engineering, experimentation, and production-quality coding.
• Ability to operate as both a people leader and technical leader, with the credibility to dive deep into details while also setting direction.
• Strong business judgment and the ability to connect technical work to product outcomes, partner value, and operational priorities.
• Experience working cross-functionally with engineering, product, senior leadership, and go-to-market teams.
• Comfort operating in a fast-moving environment where timelines are aggressive, ambiguity is common, and domain insight is as important as methodology.
• Excellent communication skills, including the ability to explain complex technical decisions and analytical findings to both technical and non-technical stakeholders.
• Interest in fraud, financial risk, identity verification, and real-time decision systems.