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Quantitative Jobs in Ohio (NOW HIRING)

Northwest

Sr. Quantitative Analyst

Columbus, OH · On-site

OH0713 NW Bancshares HQ, PA0258 Bellevue The Senior Quantitative Analyst II is responsible for contributing to and managing large projects related to the support, development, and maintenance of ...

US Bank

Quantitative Model Analyst - AML

Columbus, OH · On-site

The person in this role will join a team of Financial Intelligence Unit (FIU) quantitative analysts responsible for the ongoing monitoring, testing, analytical review, and governance of AML ...

US Bank

Quantitative Model Analyst 2

Columbus, OH · On-site

... quantitative field, and three or more years of relevant experience OR - MA/MS in a quantitative field, and less than three years of related experience Preferred Skills/Experienc e Experience with ...

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All JobsQuantitative JobsQuantitative Jobs in Ohio

Quantitative information

See Ohio salary details

$29.5K

$86.1K

$138.8K

How much do quantitative jobs pay per year?

As of Aug 29, 2026, the average yearly pay for quantitative in Ohio is $86,113.00, according to ZipRecruiter salary data. Most workers in this role earn between $33,300.00 and $113,100.00 per year, depending on experience, location, and employer.

What is a quantitative job?

Quantitative jobs, often referred to as 'Quant' roles, involve the application of mathematical, statistical, and computational techniques to analyze data and solve complex problems, particularly in finance, investment banking, and technology sectors. Professionals in these roles develop and implement models to inform trading strategies, manage risk, or optimize investment portfolios. Quants typically have strong backgrounds in mathematics, statistics, computer science, or engineering and use programming languages such as Python, R, or C++. These positions are highly analytical and require a deep understanding of financial markets, data analysis, and quantitative modeling.

What are the key skills and qualifications needed to thrive as a quantitative analyst?

To thrive as a Quantitative Analyst, you need strong mathematical, statistical, and analytical skills, usually supported by a degree in mathematics, finance, engineering, or a related field. Proficiency with programming languages like Python or R, experience with data analysis tools, and familiarity with financial modeling platforms are typically required. Exceptional problem-solving abilities, attention to detail, and effective communication skills help individuals excel in this role. These competencies are crucial for developing accurate models, interpreting complex data, and driving data-driven decisions in finance and business.

What are some common challenges faced by quantitative analysts when working with large datasets in finance?

Quantitative analysts often deal with vast and complex datasets, which can present challenges such as data quality issues, integration of disparate data sources, and ensuring computational efficiency. Successfully cleaning and validating data is essential to produce reliable models. Additionally, quant analysts must stay updated with the latest programming tools and statistical techniques to optimize data processing and derive meaningful insights, often collaborating closely with IT and software engineering teams to implement scalable solutions.

What is the difference between Quantitative vs Quantitative Analyst?

AspectQuantitativeQuantitative Analyst
Required CredentialsMathematics, statistics, or related degreesMathematics, statistics, or finance certifications
Work EnvironmentResearch, data analysis, modelingFinancial firms, investment banks, hedge funds
Industry UsageBroadly used in finance, tech, researchPrimarily in finance and investment sectors
Common Search IntentGeneral quantitative rolesSpecific finance-focused roles

Quantitative refers broadly to roles involving mathematical and statistical analysis across various industries. A Quantitative Analyst, however, is a specialized role within finance that applies quantitative methods to develop trading strategies, risk management, and financial modeling. While both share similar credentials and work environments, the analyst role is more industry-specific, focusing on financial markets and investment decision-making.

What are the most commonly searched types of Quantitative jobs in Ohio?

The most popular types of Quantitative jobs in Ohio are:

What cities in Ohio are hiring for Quantitative jobs?

Cities in Ohio with the most Quantitative job openings:

Quantitative jobs near you
Infographic showing various Quantitative job openings in Ohio as of August 2026, with employment types broken down into 85% Full Time, 13% Part Time, and 2% Contract. Highlights an 71% Physical, 6% Hybrid, and 23% Remote job distribution, with an average salary of $86,113 per year, or $41.4 per hour.

Quantitative Analyst

Beavercreek, OH • On-site

Wright-Patt Credit Union
Finance and Insurance • 1 - 5K employees

5.8

Company rating: 5.8 out of 10

Based on 8 frontline employees who took The Breakroom Quiz

Respectful managers

Uninterrupted breaks

Learn more about Wright-Patt Credit Unionexternal

Full-time

Re-posted 6 days ago

Job description

The Quantitative Analyst is responsible for leading high-impact statistical analysis, measurement design, and scalable analytics solutions that improve business performance and decision-making. This role partners closely with Strategy, Product, and Technology teams to evaluate key initiatives, identify performance drivers, develop statistically sound measurement approaches, and deliver executive-ready insights that influence priorities and investments. The Quantitative Analyst combines strong analytical depth with automation and repeatability, ensuring insights are accurate, timely, and operationally useful.

1)      High-Impact Quantitative Analysis & Decision Science (30%): Use statistical methods to identify drivers of performance, validate hypotheses, and quantify the impact of business decision using structured and repeatable approaches.

a)       Perform exploratory data analysis, segmentation, and trend analysis to uncover patterns and anomalies.

b)      Apply statistical techniques such as hypothesis testing, confidence intervals, correlation, and regression analysis.

c)       Identify opportunities for growth, efficiency, and experience improvement using data-backed recommendations.

d)      Deliver decision-ready outputs that connect analysis to actions, tradeoffs, and expected outcomes.

2)      Experimentation, Testing, and Impact Evaluation (25%): Design measurement frameworks that ensure the organization can track initiative performance, quantify impact, and drive accountability.

a)       Support A/B testing and experiment analysis including test design inputs, lift measurement, and interpretation.

b)      Partner with product and business teams to define success metrics, baselines, and measurement plans.

c)       Evaluate initiative effectiveness using controlled comparisons, pre/post analysis, and statistical significance testing.

d)      Develop standardized experiment readouts and decision frameworks to improve speed and consistency.

3)      Predictive Analytics & Optimization (20%): Drive advanced analytics efforts that improve targeting, prioritization, and decision-making through modeling and quantitative scoring.

a)       Partner with data scientists to support model development by preparing datasets, validating features, and interpreting outputs.

b)      Build and maintain scoring frameworks (propensity, prioritization, classification support) aligned to business use cases.

c)       Support model evaluation using practical performance measures (lift, precision/recall, error rates).

d)      Translate model outputs into actionable recommendations and operational workflows.

4)      Automation & Scalable Analytics Delivery (15%): Increase speed, consistency, and reliability of insights by automating analysis workflows and enabling scalable analytics delivery.

a)       Develop automated analysis workflows using SQL and Python to reduce manual effort.

b)      Build reusable scripts, templates, and standardized datasets to improve reliability and consistency.

c)       Partner with data engineering teams to improve data availability and support repeatable pipelines.

d)      Implement monitoring and alerting for key performance indicators and threshold-based changes.

5)      Communication, Visualization, and Executive Enablement (10%): Present actionable insights to senior leadership in a format that is relevant for the audience.

a)       Build clear, executive-ready summaries and visualizations tied to business outcomes.

b)      Present findings and recommendations to senior leaders and cross-functional teams.

c)       Communicate confidence levels, limitations, and tradeoffs in a practical way.

d)      Ensures proper policies, procedures, risk mitigation activities, and operating controls are followed. Reports gaps in policies, procedures, and operating controls to leadership to ensure member impact and risk is mitigated.  

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