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Quantitative Data Engineer Jobs in Ohio (NOW HIRING)

Bachelor's Degree In Data Science, Statistics, Computer Science, Mathematics, Physics, Engineering, or a related quantitative field Required * Master's Degree In Data Science, Statistics, Computer ...

Bachelor's Degree In Data Science, Statistics, Computer Science, Mathematics, Physics, Engineering, or a related quantitative field Required * Master's Degree In Data Science, Statistics, Computer ...

The position is suited for an engineer who works with complex systems and partners with Risk, Quant, Data, and Infrastructure teams. The successful candidate will be expected to become a subject ...

Bachelor's degree (or its equivalent) in statistics, mathematics, economics, financial engineering, data sciences, predictive modeling, or other quantitative disciplines and at least 1 year of ...

Bachelor's degree (or its equivalent) in statistics, mathematics, economics, financial engineering, data sciences, predictive modeling, or other quantitative disciplines and at least 1 year of ...

Showing results 21-40

Quantitative Data Engineer information

What is a quantitative data engineer?

A Quantitative Data Engineer is a professional who designs, builds, and maintains data infrastructure that supports quantitative analysis, typically in finance or technology sectors. They work closely with quantitative analysts and data scientists to ensure efficient data pipelines, data quality, and high-performance systems for processing large datasets. Their responsibilities include developing ETL processes, optimizing databases, and implementing data models to support research and trading strategies. Strong programming skills, expertise in big data technologies, and knowledge of quantitative methods are essential for this role.

How does a quantitative data engineer typically collaborate with data scientists and quantitative analysts on projects?

Quantitative Data Engineers work closely with data scientists and quantitative analysts to design, build, and optimize data pipelines that support complex modeling and analytics. They are often responsible for ensuring data quality, scalability, and efficient data processing, enabling analysts to focus on developing models and extracting insights. Regular collaboration includes translating analytical requirements into technical solutions, troubleshooting data issues, and iterating on data infrastructure to support evolving project needs. This teamwork fosters an environment where technical and analytical expertise complement each other, leading to more robust and actionable results.

What are the key skills and qualifications needed to thrive as a quantitative data engineer, and why are they important?

To excel as a Quantitative Data Engineer, you need strong proficiency in programming (such as Python, R, or C++), advanced mathematical and statistical knowledge, and a relevant degree in computer science, mathematics, or a related field. Experience with big data tools (like Spark, Hadoop), cloud platforms, and data pipeline systems, as well as familiarity with financial data sets, is typically required. Analytical thinking, detail orientation, and effective problem-solving skills distinguish top performers in this role. These competencies are critical for efficiently transforming complex data into actionable insights and supporting robust quantitative models in data-driven environments.

What is the difference between Quantitative Data Engineer vs Data Scientist?

AspectQuantitative Data EngineerData Scientist
Primary FocusBuilding data pipelines, data infrastructure, and ensuring data qualityAnalyzing data, creating models, and deriving insights
Skills & ToolsSQL, Python, Spark, ETL processes, data architectureStatistics, machine learning, Python/R, data visualization
CredentialsComputer science, engineering, or related degrees; certifications in data engineeringStatistics, data science, or related degrees; certifications in data analysis or machine learning
Work EnvironmentData engineering teams, data infrastructure projectsData analysis teams, research, and modeling projects

While both roles work closely with data, Quantitative Data Engineers focus on building and maintaining data systems, whereas Data Scientists analyze data to generate insights and models. They often collaborate but have distinct skill sets and responsibilities within data-driven organizations.

What cities in Ohio are hiring for Quantitative Data Engineer jobs?

Cities in Ohio with the most Quantitative Data Engineer job openings:

Lead Data Scientist

Columbus, OH

Safelite Group
Retail • 10K+ employees

Full-time

Re-posted 20 days ago


Safelite rating

6.6

Company rating: 6.6 out of 10

Based on 246 frontline employees who took The Breakroom Quiz


Job description

Does this position interest you? You should apply - even if you don't match every single requirement! We're known as an auto glass company. That's the focus of what we do. But beyond the glass, we're so much more. We'll help you build a fulfilling career and encourage you to have a life. Let us be the best place you'll ever work.

Does this position interest you? You should apply - even if you don't match every single requirement! We're known as an auto glass company. That's the focus of what we do. But beyond the glass, we're so much more. We'll help you build a fulfilling career and encourage you to have a life. Let us be the best place you'll ever work.
A Brief Overview
The Lead Data Scientist serves as a technical leader responsible for developing, deploying, and scaling advanced analytical, machine learning, and optimization solutions that drive measurable, profitable outcomes across Safelite's Consumer Sales & Pricing activities. The role owns endtoend data science solutions-from problem framing through production deployment-while translating complex analytical outputs and AI innovation into actionable business insights. This position plays a critical role in pricing optimization, experimentation strategy, and advancing the organization's analytics capabilities through technical leadership, mentorship, and innovation.
What You Will Do

  • Lead the development of advanced machine learning models, statistical frameworks, and optimization solutions to support consumer and sales growth. Define and enforce best practices for model development, validation, deployment, and monitoring.

  • Drive innovation through the application of cuttingedge techniques, including: Deep learning, Natural language processing (NLP), Causal inference, Reinforcement learning and emerging AI technologies.

  • Serve as the technical escalation point for complex analytical and modeling challenges

  • Continuously evaluate emerging methods and ensure their practical applicability to business problem

  • Own the full lifecycle of data science solutions: problem framing, feature engineering, model development, production deployment, ongoing monitoring and improvement

  • Translate ambiguous, highlevel business questions into structured analytical approaches

  • Ensure models are scalable, performant, explainable, and maintainable in production environments

  • Partner with engineering and platform teams to operationalize models

  • Work closely with senior business, sales, and product stakeholders to identify highvalue use cases. Translate complex model outputs into actionable insights and clear strategic recommendations.

  • Quantify business impact and ensure alignment with organizational KPIs, revenue goals, and growth strategies.

  • Influence decisionmaking through compelling, datadriven narratives, not just technical outputs

  • Mentor both junior data scientists on advanced analytical methodologies and software engineering and coding best practices

  • Contribute to building a highperformance analytics culture

  • Lead knowledge sharing through code reviews, technical standards, and design discussions

  • Collaborate with data engineering, platform, and architecture teams to define data requirements, pipelines, and scalable analytics architecture

  • Advocate for data quality, governance, and reproducibility and documentation standards

  • Evaluate and integrate new tools, frameworks, and technologies into the analytics ecosystem where they deliver clear value

  • Performs other duties as assigned

  • Complies with all policies and standards


What You Will Need

  • Bachelor's Degree In Data Science, Statistics, Computer Science, Mathematics, Physics, Engineering, or a related quantitative field Required

  • Master's Degree In Data Science, Statistics, Computer Science, Mathematics, Physics, Engineering, or a related quantitative field Preferred

  • 7-9 years Experience in data science, machine learning, applied research, or advanced analytics Required

  • Proficient in SQL

  • Advanced experiencewithtopstatisticalprogramminglanguages(RorPython)

  • Experience working in cloudbased analytics environments

  • Handson experience building Regression models, Classification models, Clustering models

  • Strong understanding of machine learning algorithms, statistical modeling, and optimization techniques

  • Experience with A/B, multiarm, and prepost testing

  • Familiarity with ML operations (MLOps), including versioning, monitoring, and CI/CD pipelines

  • Familiarity with GenAI/LLMs for price recommendation explainability; competitive intelligence from unstructured data

  • Previous work on pricing strategy, pricing optimization, or pricing engines

  • Experience designing experiments without commercial testing platforms

  • Experience collaborating with data engineering and data management teams to deploy models in production, monitor model drift, and implement re-training cadences.

Expected Work Location (In Office): It is expected that you will primarily perform work at the Safelite Home Office (7400 Safelite Way, Columbus, OH 43235). You are required to work in the office at least 4 days a week. Changes to work location arrangements are subject to managerial approval and business needs. #LI-Onsite #LI-JR2


This job description in no way states or implies that these are the only duties to be performed by an employee occupying this position. Employees may be required to perform other related duties as assigned to ensure workload coverage. This job description does NOT constitute an employment agreement between the employer and employee and is subject to change by the employer as the organizational needs and requirements of the job change.
This position description is not all inclusive for every aspect of this role. Reasonable accommodations will be made for individuals covered by ADA, ADEA, FMLA and other laws and regulations in accordance with their requirements. Physical and mental demands are not, and should not be construed to be job qualification standards, but are illustrated to help the employer, employee and/or applicant identify tasks where reasonable accommodations may need to be made when an otherwise qualified person is unable to perform the job's essential duties because of an ADA disability.
Other qualifications may be required to ensure employment eligibility in accordance with local laws, regulations and with Safelite Group, Inc. policies and practices.


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