Senior Data Scientist - Detection & Modeling

TWG Global AI•Santa Monica, CA

6d•$190,000 - $290,000•Onsite

About The Position

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

Requirements

Strong feature engineering and applied ML on transactional or time-series data
Hands-on experience with anomaly detection and unsupervised methods (e.g., isolation forests, density-based methods, autoencoders)
Solid statistical foundations, including model calibration and working with imperfect labels
Production ML experience — you ship, monitor, and maintain models over time
Comfort scaling a modeling approach across many detectors, market types, and venues, rather than building one-off models

Nice To Haves

Fraud, risk, integrity, or market-surveillance domain experience a plus
FIX / market-microstructure fluency a plus; on-chain data familiarity a plus

Responsibilities

Design and refine features on large-scale financial time-series and on-chain data, with an emphasis on signals that hold up under noisy, heavy-tailed conditions
Develop and improve anomaly-detection models across both supervised and unsupervised approaches
Evolve the model architectures as labels accumulate — from simpler classifiers toward multi-class, per-scenario, and ensemble designs; on the DeFi side, activate the supervised layer from a standingार्ट start as the first disposition labels arrive
Improve detector calibration so scores are trustworthy and comparable across market types and venues
Partner with the validation and analyst teams on the labeling pipelines that supply the models' training signal