Lead Specialist, Data Scientist

Pearson•Bloomington, MN

2d•$165,000 - $205,000

About The Position

Pearson Professional Assessments is seeking an experienced Data Scientist to help advance our data forensics, fraud detection, and exam security analytics capabilities. This role will focus on identifying emerging threats, developing predictive models, and generating actionable intelligence that protects the integrity of high-stakes testing programs worldwide. The ideal candidate is passionate about solving complex analytical challenges, leveraging machine learning techniques, and transforming large datasets into meaningful insights that drive operational and security outcomes.

Requirements

Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related field.
3+ years of experience in data science, fraud analytics, security analytics, risk analytics, or a related discipline.
Strong proficiency with Python
Strong proficiency with SQL
Strong proficiency with Statistical Analysis
Strong proficiency with Machine Learning
Strong proficiency with Databricks or similar analytics platforms
Experience working with large, complex datasets.
Strong problem-solving and analytical skills.
Ability to communicate complex findings to diverse audiences.

Nice To Haves

Master's degree in a quantitative discipline.
Experience in fraud detection, cybersecurity, investigations, digital identity, or risk management.
Experience with XGBoost
Experience with Scikit-Learn
Experience with Spark
Experience with Azure Data Services
Experience with Splunk
Experience with Graph Analytics
Knowledge of anomaly detection methodologies.
Experience supporting investigative or intelligence functions.

Responsibilities

Develop statistical and machine learning models to identify suspicious testing behavior, fraud patterns, and emerging threats.
Create and maintain risk scoring methodologies that support investigative prioritization.
Analyze candidate, test center, proctoring, identity verification, and operational datasets to identify anomalies and indicators of compromise.
Perform exploratory data analysis to uncover hidden trends and relationships.
Support complex investigations involving exam misconduct, proxy testing, collusion, content theft, account compromise, and other security concerns.
Develop repeatable analytical methodologies that improve investigative effectiveness and consistency.
Identify behavioral patterns and indicators associated with fraudulent activity.
Design, develop, validate, and deploy predictive and anomaly detection models.
Continuously monitor and improve model performance.
Utilize supervised and unsupervised learning techniques to detect previously unknown threats.
Partner with technical teams to operationalize analytical solutions.
Collaborate with security, integrity, and operational teams to identify emerging risk trends.
Research new fraud tactics and evolving threat vectors.
Translate analytical findings into actionable intelligence and business recommendations.
Develop dashboards, visualizations, and executive reporting to communicate insights.
Present analytical findings to technical and non-technical stakeholders.
Support strategic decision-making through data-driven recommendations.