Data Science Lead

Overview

Permanent, full-time, hybrid.

Connecting clients to markets – and talent to opportunity

With 5,400+ employees and over 80,000 institutional, commercial, and payments clients, we operate from more than 80 offices spread across six continents. As a Fortune 100, Nasdaq-listed provider, we connect clients to the global markets – focusing on innovation, human connection, and providing world-class products and services to all types of investors.

Whether you want to forge a career connecting our retail clients to potential trading opportunities, or ingrain yourself in the world of institutional investing, StoneX Group is made up of four business segments that offer endless potential for progression and growth.

Business segment: Engage in a deep variety of business-critical activities that keep our company running efficiently. From strategic marketing and financial management to human resources and operational oversight, you’ll have the opportunity to optimize processes and implement game-changing policies.

Responsibilities

Position Purpose: We are seeking a Data Science Lead to join the Global Data & Insights team in Bogota. This role will lead a small team delivering applied analytics, machine learning, and data product solutions for global business and technology stakeholders.

The Data Science Lead is accountable for both technical quality and delivery outcomes. This person will take business and technical priorities from intake through execution, structure the work for the team, guide day-to-day delivery, manage stakeholder expectations, and ensure work is completed to a standard that can be adopted, supported, and scaled.

This is a leadership role for someone who is still close enough to the work to challenge assumptions, review technical approaches, unblock the team, and connect model development to real business outcomes.

Primary duties will include:

• Lead, coach, and develop a small team of data scientists and analysts, creating clear expectations for ownership, quality, communication, and delivery.
• Translate business and technology priorities into well-scoped data science workstreams, including problem definition, success criteria, required data, delivery plan, and stakeholder alignment.
• Assign and guide work across the team, monitor progress, remove blockers, and ensure delegated work reaches completion without requiring continuous escalation.
• Provide technical leadership across exploratory analysis, feature definition, model development, evaluation, documentation, and implementation handoff.
• Partner with business stakeholders to understand operational, commercial, market-data, or workflow problems and determine where analytics or machine learning can create practical value.
• Collaborate with data engineering, application engineering, and platform teams on data pipelines, model-serving patterns, API integration, testing, and production readiness.
• Ensure models and analytical outputs are documented clearly, including assumptions, limitations, business context, model logic, implementation notes, and support considerations.
• Communicate progress, risks, tradeoffs, and recommendations clearly to both technical and non-technical stakeholders.

Representative Areas of Work

• Machine learning classification, scoring, prediction, and decision-support use cases.
• Operational analytics, including exception analysis, settlement or failed-trade analysis, and workflow optimization opportunities.
• News, content, market-data, or commercial analytics solutions that need to move from analysis into user-facing or business-facing workflows.
• Databricks-based experimentation, model development, documentation, code review, and team handoff practices.
• Model-serving and integration patterns, including REST APIs, inference workflows, feature pipelines, and downstream system adoption.
• Batch and streaming data use cases involving collaboration across analytics, data engineering, and application teams.

Qualifications

To land this role you will need:

• Bachelor's degree in Computer Science, Data Science, Statistics, Engineering, Mathematics, Economics, Finance, or a related quantitative field, or equivalent professional experience.
• Meaningful experience in data science, machine learning, analytics, or data product delivery, with demonstrated ownership of complex analytical or ML initiatives.
• Experience leading, managing, mentoring, or formally guiding a small technical team.
• Strong hands-on capability with Python and SQL, with enough technical depth to review team output and guide solution design.
• Experience developing, evaluating, and explaining machine learning models for business or operational use