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Quantitative Data Engineer Jobs in Georgia (NOW HIRING)

4pconsultinginc

Data Scientist II

Atlanta, GA · On-site

Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, or related quantitative field * 5-10 years of experience in data science, machine learning, and statistical analysis

Home Depot

Senior Quant - Quant Research

Atlanta, GA · On-site

In this position, you will analyze marketing data to uncover trends, provide insights, and create ... with our engineering partners across ideation, development, and delivery. The team's primary ...

Invesco

Senior Engineer-1

Atlanta, GA · On-site

$100K - $138K/yr

This role will partner closely with quantitative research teams, model owners, and downstream ... This individual will work in a fast-paced environment requiring strong data engineering ...

Invesco

Senior Engineer-1

Atlanta, GA · Hybrid

$100K - $138K/yr

This role will partner closely with quantitative research teams, model owners, and downstream ... Ensuring data quality, completeness, and traceability through proper logging, monitoring, and ...

PrizePicks

Lead Quantitative UX Researcher

Atlanta, GA · On-site +1

$150K - $185K/yr

In this role, you will own the quantitative research strategy for major product areas, conducting ... programming packages such as R or Python for custom analytics and data manipulation. * Survey ...

SOLTECH

Data Scientist

Duluth, GA · On-site

... engineering while making a meaningful impact in the utilities and sustainability space. Key ... or a related quantitative field, or equivalent combination of education and experience About ...

Worldpay, Inc.

Data Scientist Sr Lead

Atlanta, GA · Hybrid

Lead independent quantitative research initiatives, leveraging multiple data sources to generate innovative insights and identify new business opportunities. * Partner with product, engineering ...

FIS

Data Scientist Sr Lead

Atlanta, GA · Hybrid

Lead independent quantitative research initiatives, leveraging multiple data sources to generate innovative insights and identify new business opportunities. * Partner with product, engineering ...

STEMtech

Sr. Data Scientist

Atlanta, GA · On-site

Coordinates with SMEs, translators, IT and data engineers to deliver holistic analytical solutions ... quantitative discipline (e.g. advanced statistical analysis) * Experience leading a team of data ...

STEMtech

Sr. Data Scientist

Atlanta, GA

Coordinates with SMEs, translators, IT and data engineers to deliver holistic analytical solutions ... quantitative discipline (e.g. advanced statistical analysis) * Experience leading a team of data ...

Monitise

AI Engineer

Alpharetta, GA · On-site

Bachelor's degree or higher in Computer Science, Data Science, Statistics, Engineering, Cybersecurity, or a related quantitative field or equivalent combination of education, related experience and ...

Patientco

Data Scientist

Atlanta, GA

... Engineering. Incumbents whose focus is the quantitative analysis of complex business problems and issues using data from internal and external sources to provide insight to decision-makers should be ...

Crew Career Center

Data Scientist

Atlanta, GA

... Engineering. Incumbents whose focus is the quantitative analysis of complex business problems and issues using data from internal and external sources to provide insight to decision-makers should be ...

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All JobsQuantitative Data Engineer JobsQuantitative Data Engineer Jobs in Georgia

Quantitative Data Engineer information

What is an example of quantitative?

A quantitative example involves numerical data that can be measured and analyzed statistically. In a Quantitative Data Engineer role, this might include metrics like transaction volumes, data throughput, or statistical summaries used to build data pipelines and models.

What is quantitative vs qualitative?

Quantitative data refers to numerical information that can be measured and analyzed statistically, such as sales figures or sensor readings. Qualitative data involves descriptive, non-numerical information like opinions, interviews, or observations. In data engineering, understanding both types helps in designing systems that process and analyze diverse data sources effectively.

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 does quantitative mean?

In the context of a Quantitative Data Engineer role, 'quantitative' refers to working with numerical data and statistical methods to analyze and model information. This involves skills in mathematics, programming, and data analysis tools to develop algorithms and insights from large datasets.

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.

What are the synonyms of quantitative?

Synonyms of quantitative include numerical, measurable, and statistical. In a data engineering context, these terms relate to data that can be quantified and analyzed using tools like SQL, Python, or R, often involving numerical datasets and statistical methods.

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.

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 popular job titles related to Quantitative Data Engineer jobs in Georgia? For Quantitative Data Engineer jobs in Georgia, the most frequently searched job titles are:
What job categories do people searching Quantitative Data Engineer jobs in Georgia look for? The top searched job categories for Quantitative Data Engineer jobs in Georgia are:
What cities in Georgia are hiring for Quantitative Data Engineer jobs? Cities in Georgia with the most Quantitative Data Engineer job openings:
Quantitative Data Engineer jobs near you

Data Scientist II

4pconsultinginc

Atlanta, GA • On-site

Contractor

Posted 22 days ago

Job description

Position: Data Scientist II


Location: 30 Ivan Allen Jr Blvd, NW, Atlanta GA, 30308 HYBRID
Duration:  1 Year

Client: Southern Company

Position Overview

The Data Scientist II is responsible for leveraging large and complex datasets to uncover insights, develop predictive models, and drive data-informed decision-making across the organization.

This role requires strong expertise in advanced analytics, machine learning, statistical modeling, and data engineering principles. The ideal candidate is a strategic thinker who can translate business challenges into scalable analytical solutions while mentoring junior team members.

Key Responsibilities

Data Analysis & Insight Generation

  • Collect, clean, and analyze complex datasets
  • Identify trends, patterns, and actionable insights
  • Apply statistical techniques to support data-driven decisions

Predictive Modeling & Machine Learning

  • Develop and deploy machine learning models to predict future trends and outcomes
  • Apply regression, clustering, classification, and advanced modeling techniques
  • Build and optimize algorithms such as:
    • Decision Trees
    • Random Forests
    • Neural Networks
    • Gradient Boosting models

Feature Engineering & Model Optimization

  • Engineer and select relevant features to improve model performance
  • Fine-tune model parameters and validate predictive accuracy
  • Ensure models are scalable and production-ready

Model Deployment & Production Support

  • Deploy machine learning models into production environments
  • Support real-time decision-making applications
  • Monitor model performance and retrain as needed

Data Visualization & Communication

  • Develop dashboards and visualizations using Tableau, Power BI, or Python libraries (Matplotlib, Seaborn, etc.)
  • Communicate insights effectively to technical and non-technical stakeholders

Hypothesis Testing & Experimentation

  • Design and analyze A/B tests
  • Conduct hypothesis testing and provide statistical validation
  • Measure business impact of changes and enhancements

Data Integration & Collaboration

  • Collaborate with IT and database teams to access and integrate data sources
  • Work with cross-functional teams (engineering, business analysts, domain experts)
  • Align data science initiatives with strategic business objectives

Governance & Ethics

  • Ensure ethical data practices and compliance with data privacy regulations
  • Maintain documentation and transparency in model development

Leadership & Development

  • Mentor junior data scientists and analysts
  • Contribute to best practices and data science methodologies
  • Stay current with emerging tools, technologies, and industry trends

Required Qualifications

  • Bachelor’s degree in Computer Science, Statistics, Mathematics, Engineering, or related quantitative field
  • 5–10 years of experience in data science, machine learning, and statistical analysis
  • Proficiency in Python, R, or Julia
  • Strong understanding of machine learning algorithms and their applications
  • Experience with SQL and database querying
  • Experience with data visualization tools (Tableau, Power BI, or Python libraries)
  • Strong analytical, problem-solving, and critical-thinking skills
  • Excellent written and verbal communication skills

Preferred Qualifications

  • Master’s or Ph.D. in a quantitative field
  • Experience with big data technologies (Hadoop, Spark)
  • Experience with distributed computing frameworks
  • Experience deploying models in cloud environments