Senior Data Scientist - Detection & Modeling

TWG Global AI

$190K — $290K *
Finance & Insurance
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 5-7 years of experience in feature engineering and applied machine learning on transactional or time-series data
  • Proven hands-on experience with anomaly detection and unsupervised learning methods
  • Strong statistical background, especially in model calibration
  • Experience deploying, monitoring, and maintaining machine learning models in production
  • Ability to scale modeling approaches across various market types and detectors
  • Experience in fraud, risk, integrity, or market surveillance is advantageous
  • Knowledge of FIX protocol or market microstructure is a plus, familiarity with on-chain data is highly desirable

Responsibilities

  • Design and refine features for large-scale financial and on-chain data, focusing on resilience to noise
  • Develop and enhance anomaly detection models using both supervised and unsupervised methods
  • Advance model architectures as new labeled data becomes available
  • Ensure detector calibration yields trustworthy scores across different markets
  • Collaborate with validation and analyst teams to optimize training signal labeling

Benefits

  • Onsite position based in Santa Monica, CA or New York, NY
  • Comprehensive medical benefits
  • Financial benefits, including bonuses and a competitive compensation package
Full Job Description
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.

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