Senior Data Scientist - Detection & Modeling

Location: Santa Monica, California, United States, New York, New York, United States

Department: AI/ML Scientists

Workplace: on_site

Employment Type: full

Description

The Organization

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. 

The Role

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 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 standing 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

Requirements

Qualifications:

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

Benefits

Position Location: 

This is an onsite position based out of our Santa Monica, CA or New York, NY offices.

Compensation: 

The base pay for this position is $190,000-290,000. A bonus will be provided as part of the compensation package, in addition to a full range of medical, financial, and/or other benefits. 

TWG is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.