Minimum of three (3) years' experience in customer analytics domain and/or credit risk assessment ... Degree in quantitative discipline such as Statistics, mathematics, Operations Research, Engineering ...
Minimum of three (3) years' experience in customer analytics domain and/or credit risk assessment ... Degree in quantitative discipline such as Statistics, mathematics, Operations Research, Engineering ...
Minimum of three (3) years' experience in customer analytics domain and/or credit risk assessment ... Bachelor's Degree in quantitative discipline such as Statistics, mathematics, Operations Research ...
Minimum of three (3) years' experience in customer analytics domain and/or credit risk assessment ... Bachelor's Degree in quantitative discipline such as Statistics, mathematics, Operations Research ...
Quantitative Risk Manager information
See Miami, AZ salary details
$49.8K - $60.2K
4% of jobs
$60.2K - $70.6K
6% of jobs
$70.6K - $81K
11% of jobs
$84.9K is the 25th percentile. Wages below this are outliers.
$81K - $91.4K
11% of jobs
The median wage is $99.7K / yr.
$91.4K - $101.8K
23% of jobs
$101.8K - $112.2K
13% of jobs
$119.1K is the 75th percentile. Wages above this are outliers.
$112.2K - $122.7K
12% of jobs
$122.7K - $133.1K
8% of jobs
$133.1K - $143.5K
6% of jobs
$143.5K - $153.9K
4% of jobs
$153.9K - $164.3K
2% of jobs
$49.8K
$107.8K
$164.3K
How much do quantitative risk manager jobs pay per year?
How does a quantitative risk manager typically collaborate with other departments within a financial institution?
What are the key skills and qualifications needed to thrive as a quantitative risk manager, and why are they important?
What is a quantitative risk manager?
What is the difference between Quantitative Risk Manager vs Quantitative Analyst?
| Aspect | Quantitative Risk Manager | Quantitative Analyst |
|---|---|---|
| Primary Focus | Assessing and managing risk exposure across financial portfolios | Developing models and algorithms for investment strategies |
| Required Credentials | Advanced degrees in finance, mathematics, or related fields; certifications like FRM or CFA | Degrees in finance, mathematics, or statistics; often pursuing CFA or similar |
| Work Environment | Financial institutions, risk management departments | Investment firms, hedge funds, banks |
| Key Skills | Risk assessment, regulatory knowledge, quantitative modeling | Data analysis, programming, financial modeling |
While both roles involve quantitative skills and financial knowledge, Quantitative Risk Managers focus on identifying and mitigating risks within organizations, whereas Quantitative Analysts primarily develop models to inform investment decisions. Understanding these differences helps professionals choose the right career path or job search focus.
What cities near Miami, AZ are hiring for Quantitative Risk Manager jobs?
Cities near Miami, AZ with the most Quantitative Risk Manager job openings:
Full-time
Re-posted 7 days ago
Job description
At Globe, our goal is to create a wonderful world for our people, business, and nation. By uniting people of passion who believe they can make a difference, we are confident that we can achieve this goal.
Job Description The Data Science Manager leads the end-to-end development of data-driven solutions, from translating business needs into data science projects, to building, deploying, and monitoring predictive models in production. This role ensures models deliver measurable business impact, maintains the reliability of machine learning pipelines, and collaborates closely with business, product, and engineering teams to integrate data science solutions into operational systems.DUTIES AND RESPONSIBILITIES:
Data Exploration & Feature Engineering
Lead data extraction, exploration, cleansing, and transformation of large and complex datasets
Design, engineer, and validate features needed for predictive models and advanced analytics
Build frameworks and data pipelines that combine telco datasets with digital, social, and external data sources to create a holistic customer view.
Model Development & Performance Management
Translate business problems into clear data science approaches and model requirements
Build, test, and deploy machine learning and statistical models that address business needs
Track performance, accuracy, drift, and business value of deployed models
Conduct periodic model tuning and ensure continuous improvement aligned with ROI goals.
Insights, Applications & Business Enablement
Translate model outputs into clear insights, actionable recommendations, and campaign or operational strategies
Identify new opportunities to apply data science, especially in customer behavior prediction, segmentation, and credit risk scoring.
Partner with business teams to embed analytics solutions into decision-making and customer lifecycle programs.
REQUIREMENTS:
Work Experience
Minimum of three (3) years' experience in customer analytics domain and/or credit risk assessment and financial services, covering most of the following: data mining, predictive modeling, machine learning, statistical modeling and analysis, large scale data acquisition, transformation, and cleaning, both structured and unstructured data
Proven track record of leading and collaborating on advanced analytics strategic initiatives; Proven track record of operationalization of analytic models in collaboration with marketing/risk and IT teams
Worked with large, unfiltered data sets or data science research
Level of Knowledge
Has Knowledge of both structured and unstructured data
Must possess core competencies, deep understanding and relevant experience in
Scripting or programming experience: familiarity in programming languages with relational databases (e.g. Python, Java, Ruby, Clojure, Matlab, Pig, SQL);
Statistical Analysis: advanced usage of off-the-shelf tools such as R, SAS, SPSS, Weka and other analytical tools or software
Big Data: Experience with Big data tools such as HDFS, Cassandra, Storm
Database knowledge: skilled in structured database
Familiar with most of the following disciplines:
Conceptual modeling: to be able to share and articulate modeling;
Predictive modeling: most of the big data problems are towards being able to predict future outcomes;
Hypothesis testing: being able to develop hypothesis and test them with careful experiments;
Natural Language Processing: the interactions between computer and humans;
Machine learning: using computers to improve as well as develop algorithms;
Statistical analysis: to understand and work around possible limitations in models.
Education
Degree in quantitative discipline such as Statistics, mathematics, Operations Research, Engineering, Computer Science, Econometrics or Information Science such as Business Analytics or Informatics
Equal Opportunity Employer
Globe's hiring process promotes equal opportunity to applicants, Any form of discrimination is not tolerated throughout the entire employee lifecycle, including the hiring process such as in posting vacancies, selecting, and interviewing applicants.
Globe's Diversity, Equity and Inclusion Policy Commitment can be accessed here
Make Your Passion Part of Your Profession. Attracting the best and brightest Talents is pivotal to our success. If you are ready to share our purpose of Creating a Globe of Good, explore opportunities with us.