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Research Python Jobs in Atlanta, GA (NOW HIRING)

Title: Python Engineer Loc: Alpharetta, GA Mode of work: Remote Your key responsibilities ... MsoNormal">Research, explore, and implement new methods and processes to build world-class data ...

New

Develop solutions using Python in a cloud-based ecosystem. * Actuarial Data Support: Collaborate ... Experience working in an actuarial or research field in a data engineering role supporting advanced ...

Overview DLH Corp Company, is looking for a part-time Public Health Research Analyst , using SAS and R and Python , to join our talented and innovative team supporting the Centers for Disease Control ...

Java and Python

Atlanta, GA · On-site

$45 - $50/hr

Java and Python - Hands-on experience with AWS services such as EC2, Lambda, S3, RDS, CloudWatch ... Researching and recommending innovative technologies, tools, and techniques. * Assisting in ...

Overview DLH Corp Company, is looking for a part-time Public Health Research Analyst , using SAS and R and Python ,to join our talented and innovative team supporting the Centers for Disease Control ...

Research Associate I

Atlanta, GA · On-site

$47K - $52K/yr

Research Associate I Georgia State University Andrew Young School of Policy Studies Public Finance ... Experience working with quantitative datasets and statistical software (e.g., R, Stata, Python, or ...

You are a strong Python engineer who can move fluently between an experiment and a well-structured service or SDK module. You write research artefacts and production code in the same week, and you ...

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Research Python information

See Atlanta, GA salary details

$12

$56

$82

How much do research python jobs pay per hour?

As of Jun 10, 2026, the average hourly pay for research python in Atlanta, GA is $56.37, according to ZipRecruiter salary data. Most workers in this role earn between $46.44 and $64.04 per hour, depending on experience, location, and employer.

What is a Research Python Developer?

A Research Python Developer is a professional who uses the Python programming language to support and conduct research activities. They often work with data analysis, machine learning, simulation, and automation to solve scientific or academic problems. Their role may involve developing prototypes, processing large datasets, and collaborating with researchers to implement algorithms or models. Research Python Developers are commonly found in universities, research institutions, and tech companies focused on innovation.

What is the difference between Research Python vs Data Analyst?

AspectResearch PythonData Analyst
Required SkillsPython programming, research methodologies, data analysisData analysis, visualization, SQL, Excel
Work EnvironmentResearch labs, academic institutions, tech companiesBusiness settings, corporate offices, consulting firms
Common CertificationsPython certifications, research methodology coursesMicrosoft Excel, Tableau, SQL certifications
Industry UsageAcademic research, scientific projects, tech R&DBusiness intelligence, marketing, finance

Research Python focuses on using Python for scientific and academic research, emphasizing programming and research methodologies. Data Analysts primarily analyze and interpret data to support business decisions, often using tools like Excel and Tableau. While both roles require data skills, Research Python is more technical and research-oriented, whereas Data Analysts focus on data interpretation within business contexts.

What are the key skills and qualifications needed to thrive as a Research Python Developer, and why are they important?

To thrive as a Research Python Developer, you need expertise in Python programming, data analysis, and a strong foundation in mathematics or computer science, often supported by an advanced degree. Familiarity with libraries such as NumPy, pandas, TensorFlow, and version control systems like Git is typically required. Analytical thinking, problem-solving, and effective communication are crucial soft skills for translating research goals into practical code. These skills are essential for developing robust research solutions, collaborating with interdisciplinary teams, and advancing scientific or technical projects.

What are some common challenges faced by Research Python Developers when collaborating with cross-functional teams?

Research Python Developers often work alongside data scientists, domain experts, and engineers, which can present challenges such as aligning on project goals, translating research requirements into efficient code, and ensuring reproducibility of results. Effective communication and thorough documentation are key to overcoming these challenges. Additionally, Research Python Developers may need to adapt their code to integrate with different tools or platforms used by other team members, requiring flexibility and a willingness to learn new technical concepts.

Python Engineer

Skillify Solutions

Alpharetta, GA • Remote

Other

Posted 2 days ago


Job description

Title: Python Engineer

Loc: Alpharetta, GA

Mode of work: Remote

Your key responsibilities

  • Design and develop scalable Python applications for data ingestion using class-based, object-oriented architecture (master controller/services/helpers pattern), applying encapsulation, polymorphism, and dependency injection to produce maintainable, extensible code.
  • Collaboratively identify and ideate opportunities to continuously improve the data asset and the services it provides to consumers.
  • Engage in design and development sessions to further data asset creation and ingestion pipelines, with a focus on stability, optimisation, and traceability.
  • Develop, test, and maintain data pipelines and processes, writing resilient, fault-tolerant, modular code to ensure processes, pipelines, and services are robust and highly available.
  • Build and maintain RESTful API integrations for data ingestion from external commercial and open data providers.
  • Research, explore, and implement new methods and processes to build world-class data products and associated services.
  • Actively participate in cross-team collaboration to ensure smooth transition through Ingestion, Consumption, and Activation.

Skills and attributes for success

  • Expert-level Python (OOP) with deep hands-on experience building production applications: class hierarchies, abstract base classes, polymorphism, encapsulation, and dependency injection. Ability to architect and implement master controller/services/helpers application structures.
  • Python-based API development: Proven experience building and consuming RESTful APIs in Python, including authentication handling, pagination, error handling, and retry logic.
  • SQL: Strong SQL skills for data querying, transformation, and pipeline validation.
  • Azure Data Engineering concepts: Solid understanding of Azure cloud data infrastructure and storage patterns (ADLS, Azure SQL).
  • Experience building and maintaining data ingestion pipelines and processing workflows.

Preferred Skills

  • Ability to work as an independent contributor with minimal supervision.
  • Strong sense of ownership and accountability for deliverables.
  • Good interpersonal and communication skills; ability to collaborate effectively with cross-functional teams.
  • Strong logical, analytical, and problem-solving abilities.
  • Azure Databricks (ADB) / PySpark: Experience welcome for high-volume distributed processing scenarios; not required for standard ingestion workloads.
  • Familiarity with XBRL / iXBRL financial data formats.
  • Experience with collaborative code repositories (Git/GitHub) and agile/scrum tooling.

Qualifications

  • Bachelor’s degree in Computer Science, Software Engineering, Information Technology, or equivalent.
  • 5+ years of experience in software/data engineering with a strong Python development focus

Skill/Technology

Expectation

Python (OOP)

Must have

PySpark

Must have

SQL

Must have

REST API Development

Must have

Azure Data Factory (ADF)

Must have

Azure Databricks

Must have

Apache Spark

Must have

Azure Blob Storage

Must have

Azure Data Lake (ADLS Gen2)

Must have

Azure SQL / DB Storage

Must have

Data Integration across sources

Good to Have

Application Architecture

Good to Have

Code Quality & Testing

Good to Have

Data Ingestion Pipelines

Must have

Large-scale batch & streaming

Good to Have

XBRL / iXBRL

Good to Have

Git / GitHub

Good to Have

Agile / Scrum

Good to Have