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Data Scientist – Risk Modeling & Analytics

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Remote, Hybrid

Data Scientist – Risk Modeling & Analytics

soulpage IT solutions

Data Scientist – Risk Modeling & Analytics

Skills

PythonPyTorchTensorFlowData AnalysisMachine LearningNLP / Natural Language ProcessingSupply ChainCompliance

8 months ago

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Important information


Contract type:

Permanent

Location:

United States

Work mode:

Remote, Hybrid

Published on:

22 August 2026

What they need


August 19, 2025

Role: Data Scientist

Employment Type: C2C

Client: REI Systems – FDA Account

Location: Remote, USA

No. of Positions: 2

Work Experience: 5+ Years of experience

Educational Qualifications: BS in Engineering, Computer Science, Information Systems or equivalent

Visa & Work Authorization: US Citizen/Green Card Holder/H1B/GC-EAD

Residency: Must have lived in the U.S. for at least 1095 days (3 years).

Security Clearance Requirements: Eligible to obtain Public Trust Clearance

Work Location: Hybrid or Remote for very strong candidates

Contract Duration: 6 months, extendable up to 1 year.

Interview Process: Two technical rounds (Including 1 coding round) followed by client interaction – Virtual Interviews

Skills & Experience Requirements:
5+ years applying advanced statistical, machine learning, and graph analytics techniques to solve complex risk or anomaly detection problems.

Strong proficiency in Python, including ML frameworks (PyTorch, TensorFlow, scikit-learn) and graph ML libraries.

Experience with transformer-based NLP models, sentiment analysis, and entity resolution.

Ability to design and evaluate predictive models using metrics like Precision@K, ROC-AUC, F1-score, with a focus on operational impact.

Demonstrated experience in translating model insights into clear, explainable outputs for non-technical stakeholders.

Preferred Qualifications:
Hands-on work in fraud detection, compliance monitoring, or national security risk modeling.

Familiarity with imports risk screening workflows, PREDICT, or similar systems.

Experience modeling complex supply chains and applying graph-based ML to entity relationships.

Prior exposure to FDA or other federal public health agency datasets.

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