Quantitative Analyst
Share
Job Location:
Jersey, NJ - USA
Monthly Salary:
Not Disclosed
Posted on:
4 hours ago
Vacancies:
1 Vacancy
Job Summary
Title: Quantitative Analyst
Duration: 6 Months
Location: Jersey City NJ 07311
Summary:
The Insider Risk team in partnership with the Information Security Data Operations team is working on a project to centralize IR data in the Cybersecurity Data Lakehouse (CyberDW). We are looking for a Data Scientist who can work with the developers and Data Analysts to perform analytics develop risk and quant models around Insider Risk data. Ultimately we want to create a human risk score for the Insider Risk program. This individual will be adept at ML AI and best practices around the new tools in the marketplace.
The Data Scientist / Data Modeler / Quantitative Analyst will play a critical role in advancing the Insider Risk programs detection scoring and decisioning capabilities. This role is responsible for designing building and continuously improving quantitative models statistical methods and analytical frameworks used to identify assess and prioritize insider risk across employees contractors vendors and non human identities.
The role partners closely with Cyber HR Legal Compliance Anti Fraud and Enterprise Information Protection to transform complex enterprise data into defensible risk signals transparent scoring models and executive level metrics that support investigations governance and regulatory scrutiny.
Required Skills:
1) Bachelors or Masters degree in Data Science Statistics Applied Mathematics Economics Quantitative Finance Computer Science or a related discipline.
2) 5 years of experience in data science quantitative analysis or risk modeling preferably in financial services or regulated industries.
3) Strong experience building statistical or machine learning models (regression classification anomaly detection clustering).
4) Proficiency in Python and/or R with experience in SQL for large scale data analysis.
5) Hands on experience working with complex enterprise datasets and translating analytics into business decisions.
6) Strong communication skills with the ability to explain complex analytical concepts to non technical stakeholders.
7) Experience supporting Insider Risk Fraud AML Cybersecurity UEBA or Threat Analytics programs.
8) Familiarity with identity and access data endpoint telemetry DLP email or collaboration monitoring.
9) Experience with model explainability governance and validation in regulated environments.
10) Knowledge of employee lifecycle risk behavioral analytics or human centric risk modeling.
Duration: 6 Months
Location: Jersey City NJ 07311
Summary:
The Insider Risk team in partnership with the Information Security Data Operations team is working on a project to centralize IR data in the Cybersecurity Data Lakehouse (CyberDW). We are looking for a Data Scientist who can work with the developers and Data Analysts to perform analytics develop risk and quant models around Insider Risk data. Ultimately we want to create a human risk score for the Insider Risk program. This individual will be adept at ML AI and best practices around the new tools in the marketplace.
The Data Scientist / Data Modeler / Quantitative Analyst will play a critical role in advancing the Insider Risk programs detection scoring and decisioning capabilities. This role is responsible for designing building and continuously improving quantitative models statistical methods and analytical frameworks used to identify assess and prioritize insider risk across employees contractors vendors and non human identities.
The role partners closely with Cyber HR Legal Compliance Anti Fraud and Enterprise Information Protection to transform complex enterprise data into defensible risk signals transparent scoring models and executive level metrics that support investigations governance and regulatory scrutiny.
Required Skills:
1) Bachelors or Masters degree in Data Science Statistics Applied Mathematics Economics Quantitative Finance Computer Science or a related discipline.
2) 5 years of experience in data science quantitative analysis or risk modeling preferably in financial services or regulated industries.
3) Strong experience building statistical or machine learning models (regression classification anomaly detection clustering).
4) Proficiency in Python and/or R with experience in SQL for large scale data analysis.
5) Hands on experience working with complex enterprise datasets and translating analytics into business decisions.
6) Strong communication skills with the ability to explain complex analytical concepts to non technical stakeholders.
7) Experience supporting Insider Risk Fraud AML Cybersecurity UEBA or Threat Analytics programs.
8) Familiarity with identity and access data endpoint telemetry DLP email or collaboration monitoring.
9) Experience with model explainability governance and validation in regulated environments.
10) Knowledge of employee lifecycle risk behavioral analytics or human centric risk modeling.
