Senior MAQR; ML & Quantitative Research Data Scientist

At TWG Group Holdings, LLC ("TWG Global"), we drive innovation and business transformation across a range of industries—including financial services, insurance, technology, media, and sports—by leveraging data and AI as core assets. Our AI-first, cloud-native approach delivers real-time intelligence and interactive business applications, empowering informed decision-making for both customers and employees.

We prioritize responsible data and AI practices, ensuring ethical standards and regulatory compliance. Our decentralized structure enables each business unit to operate autonomously, supported by a central AI Solutions Group, while strategic partnerships with leading data and AI vendors fuel game-changing efforts in marketing, operations, and product development.

You will collaborate with management to advance our data and analytics transformation, enhance productivity, and enable agile, data-driven decisions. By leveraging relationships with top tech startups and universities, you will help create competitive advantages and drive enterprise innovation.

At TWG Global, your contributions will support our goal of sustained growth and superior returns, as we deliver rare value and impact across our businesses.

Join our growing Machine Learning, AI & Quantitative Research (MAQR) group as a Senior Data Scientist. MAQR is a small, high-ownership team embedded across a diverse portfolio of businesses — from financial services and insurance to marketplace trading platforms and operations. We don't have a narrow mandate. We build what needs to be built, ship it to production, and measure what matters.

This role is for a data scientist who is as comfortable wrangling a messy operational dataset as they are designing a production inference pipeline. You bring rigor without rigidity — you know when a logistic regression is the right answer and when it isn't. You've built things that run in the real world, not just in notebooks.

We operate with a startup mindset inside a well-capitalized holding company. You will have real ownership, real clients, and real impact.

• Own the full model lifecycle: from problem formulation and feature engineering through training, validation, and deployment in a live production environment
• Build and maintain ML pipelines that run reliably — not just models that score well offline
• Instrument models for monitoring, drift detection, and retraining triggers in production systems
• Work closely with engineers to integrate models into product workflows, operational tooling, and client-facing applications

• Develop predictive models, time series forecasts, anomaly detection systems, and causal inference frameworks across varied business contexts
• Design and analyze experiments — from A/B tests to geo-randomized trials — with proper statistical rigor
• Translate ambiguous business problems into well-scoped data science problems; define success metrics before modeling begins
• Review relevant literature and apply state-of-the-art methods where warranted; resist over engineering where simpler approaches perform just as well

• Partner with cross-functional teams — business leads, engineers, product managers, and analysts — to deliver solutions aligned with real objectives
• Translate model outputs and technical trade-offs into clear, accessible communication for business stakeholders
• Contribute to AI governance standards and champion responsible, explainable AI practices across client engagements

• Master's degree in a quantitative field (Statistics, Computer Science, Mathematics, Operations Research, Engineering, or related) plus 6+ years of applied data science experience, OR PhD in a quantitative field plus 3+ years of hands‑on industry experience
• At least 6+ years of Python (or equivalent) for data analysis, model development, and production-ready scripting
• At least 4+ years of end-to-end ML model development and deployment — not just training, but shipping to production
• Demonstrated fluency across the data science stack: wrangling, feature engineering, modeling, evaluation, and deployment…