Senior Data Scientist

Senior Data Scientist For Detection & ModelingAt 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.As a Senior Data Scientist for Detection & Modeling, you'll own the features and models at the core of the surveillance systems — the layer that turns raw trading behavior into scored, explainable signals — and you'll evolve that layer as the systems mature and the labeled datasets grow. The work now spans two detection stacks on different grains: the account-grain L1–L3 stack on the US exchange and the wallet/cluster-grain D1–D3 stack of the DeFi Integrity Engine. The two seats split by venue focus while operating as one modeling practice: shared methodology (robust statistics on heavy-tailed data, calibration discipline, the labeling loop), different substrates. This is a hands-on modeling role with a lot of room to shape the detection architecture over the life of the program.Key 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 conditionsDevelop and improve anomaly-detection models across both supervised and unsupervised approachesEvolve 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 standing start as the first disposition labels arriveImprove detector calibration so scores are trustworthy and comparable across market types and venuesPartner with the validation and analyst teams on the labeling pipelines that supply the models' training signal