Senior Data Modeler / Data Scientist (Insider Risk)

SMBC Senior Data Modeler / Data Scientist (Insider Risk)

Location:

Charlotte, North Carolina (Hybrid)- Need local within driving distance

Duration:

6 Months Visa

NO H1B MOI 2

Video Round Experience:

10+

Years Rate:

W2: $64/hr + $6 margin C2

C:

$60-62/hr.

Role Overview:

SMBC is seeking a Senior Data Modeler / Data Scientist to join the Insider Risk & Cybersecurity Data team . This role focuses on building risk models, analytics frameworks, and human risk scoring systems by leveraging data from the Cybersecurity Data Lakehouse (CyberDW). You will work closely with Cybersecurity, HR, Legal, Compliance, and Fraud teams to transform complex enterprise data into actionable insights and risk signals.

Key Responsibilities:

Design and develop quantitative models for Insider Risk detection and scoring Build and enhance statistical & machine learning models (regression, classification, clustering, anomaly detection) Develop human risk scoring frameworks for employees, contractors, and vendors Collaborate with cross-functional teams to support investigations and risk decisioning Translate complex data into business insights and executive-level reporting Ensure model governance, explainability, and regulatory compliance

Required Skills:

Bachelor s/Master s in Data Science, Statistics, Mathematics, Computer Science, or related field 5+ years of experience in Data Science / Risk Modeling / Quantitative Analysis Strong experience in Machine Learning & Statistical Modeling Proficiency in Python or R , and strong SQL skills Experience handling large-scale enterprise datasets Strong communication skills (technical non-technical translation)

Preferred Experience:

Insider Risk, Fraud, AML, Cybersecurity, UEBA, or Threat Analytics Experience with identity/access data, DLP, endpoint, email, or collaboration tools Knowledge of behavioral analytics / human risk modeling Experience in regulated environments (banking/finance) Familiarity with model validation, governance, and explainability

Ideal Candidate:

A strong Data Scientist / Quant / Data Modeler with experience in cybersecurity or financial risk domains , who can bridge data, analytics, and business risk strategy .