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# Senior Data Scientist

- **Company:** IDT
- **Location:** Newark, NJ, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Artificial Intelligence, Artificial Neural Networks, Big Data, Information Engineering, Information Leak Prevention, Database Queries, Fraud Prevention and Detection, Python (Programming Language), Machine Learning, NumPy, Cloud Services, Tensorflow, SQL Databases, Explainable AI (XAI), Feature Store, Graph Neural Networks, Pytorch, Large Language Models, Snowflake, Deep Learning, Model Validation, Pandas, Event Driven Architecture, Build Management, Scikit Learn, Information Technology, Xgboost, Machine Learning Operations
- **Published:** November 5, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pwo8zqhcti

## About the Role

IDT is expanding its AI Lab and seeking a hands-on, highly experienced Senior Data Scientist with a proven track record of building new ML models from scratch and deploying them into real-time production. You will tackle complex problems across fraud prevention, payment risk, transaction scoring, personalization, and financial forecasting using large-scale transactional datasets. You will own the full modeling lifecycle - from problem framing through to production monitoring., * Experience: 5+ years of hands-on experience in Data Science / ML, with a strong focus on designing and building new ML models from scratch and delivering them into production with measurable business impact.

 * English: B2+ (Upper-Intermediate or higher) - fluent written and spoken communication for technical discussions and daily collaboration with an international team.
 * Math & Modeling Foundations: Deep understanding of statistics, probability, and validation methodologies; practical experience handling class imbalance, model calibration, and anomaly detection.
 * Tech Stack:
 * Python: Advanced proficiency (Scikit-learn, Pandas, NumPy);
 * Deep Learning: Hands-on experience with PyTorch or TensorFlow;
 * SQL: Complex queries and large-scale data manipulation;
 * MLOps Tools: Experiment tracking and model lifecycle management (MLflow, ClearML, etc.).
 * Education: Bachelor's degree in Computer Science, Mathematics, Statistics, Engineering, or a related quantitative field.

Nice-to-Have:

 * Domain experience in FinTech, Payments, or Fraud Prevention: real-time transaction scoring and low-latency inference.
 * Experience with cloud data platforms (Snowflake), Feature Stores, and event-driven architectures.
 * Background in time-series forecasting, graph neural networks, Explainable AI (XAI), or applied LLM solutions.
 * Master's degree or Ph.D. in a quantitative discipline.

## Description

* ML & Predictive Modeling: Design and build new models from the ground up (gradient boosting, neural networks, ensembles, anomaly detection) for anti-fraud, risk assessment, and personalization; maintain rigorous validation practices to prevent data leakage and overfitting.
 * End-to-End & MLOps: Partner with Data Engineering and MLOps to operationalize models in production (batch and real-time/low-latency); build training, experiment tracking (MLflow), and model/data drift monitoring pipelines.
 * Experimentation & Business Impact: Design A/B tests and statistical evaluations to measure model performance against key business metrics (reduced fraud losses, transaction acceptance rates, decision latency).
 * R&D & Technical Leadership: Explore emerging techniques (LLMs/GenAI, graph-based methods), establish engineering best practices, and mentor fellow team members.