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Entry Level Python Data Science Jobs in Huntsville, AL

Decisive Intelligence Inc

AI Engineer

Huntsville, AL · On-site

Bachelors degree in Computer Science, Data Science, Electrical Engineering, Physics, or a related ... Proficiency in programming languages such as Python, R, or Java. * Experience with machine learning ...

PPG

Entry Level Analytical Chemist

Huntsville, AL

$21.25 - $28.50/hr

You'll partner with experienced scientists in a collaborative, safety-first environment ... Analyze experimental data, prepare clear reports and findings for multiple teams. * Troubleshoot ...

Leidos

Computer Vision AI/ML Engineer

Huntsville, AL · On-site

$69K - $125K/yr

... computer science, math, data science, physics, or engineering with 2+ years of prior relevant ... Python with additional experience in C++ and MATLAB * 1 years of experience working in a Linux ...

kgs

AI/ML Engineer

Huntsville, AL · On-site

... g., Python, FastAPI) * Design and implement RESTful APIs and contribute to database design and ... Bachelor's degree in Artificial Intelligence, Machine Learning, Computer Science, Data Science ...

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Entry Level Python Data Science information

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$57

$85

How much do entry level python data science jobs pay per hour?

As of Jun 29, 2026, the average hourly pay for entry level python data science in Huntsville, AL is $57.76, according to ZipRecruiter salary data. Most workers in this role earn between $47.60 and $65.62 per hour, depending on experience, location, and employer.

What are some common challenges faced by entry-level Python data scientists when starting out, and how can they be addressed?

Entry-level Python data scientists often encounter challenges such as managing large datasets, understanding the nuances of real-world data (like missing or inconsistent values), and effectively communicating technical findings to non-technical stakeholders. To address these challenges, it's helpful to develop strong data cleaning skills, practice using libraries like pandas and scikit-learn, and focus on improving data visualization and storytelling abilities. Additionally, seeking feedback from more experienced team members and participating in collaborative projects can accelerate learning and help overcome early hurdles.

What is an entry level Python data scientist?

An entry level Python data scientist is a professional who uses Python programming language to analyze, interpret, and visualize data, typically in the early stages of their data science career. They are responsible for collecting, cleaning, and preparing data, performing basic statistical analyses, and building simple machine learning models under supervision. These roles often require proficiency in Python libraries like pandas, NumPy, and scikit-learn, as well as good problem-solving skills. Entry level data scientists may work in industries such as finance, healthcare, marketing, or technology to help organizations make data-driven decisions.

What are the key skills and qualifications needed to thrive as an Entry Level Python Data Scientist, and why are they important?

To thrive as an Entry Level Python Data Scientist, you need a strong understanding of statistics, data analysis, and proficiency in Python programming, typically supported by a relevant degree or coursework. Familiarity with data science libraries (such as pandas, NumPy, and scikit-learn), data visualization tools, and basic SQL is commonly required. Analytical thinking, problem-solving, and effective communication help you interpret data and present findings clearly. These skills ensure you can extract meaningful insights from data, collaborate effectively, and contribute to data-driven decision-making.

What is the difference between Entry Level Python Data Science vs Entry Level Data Analyst?

AspectEntry Level Python Data ScienceEntry Level Data Analyst
Required SkillsPython, SQL, statistics, machine learning basicsExcel, SQL, data visualization, basic statistics
CertificationsPython programming, data science fundamentalsExcel certifications, basic data analysis courses
Work EnvironmentTech companies, startups, data-driven teamsBusiness departments, marketing, finance teams
Common UsageBuilding models, data cleaning, predictive analyticsReporting, data visualization, trend analysis

Entry Level Python Data Science roles focus on programming, machine learning, and predictive modeling, often requiring Python and statistical knowledge. Entry Level Data Analyst positions emphasize data reporting, visualization, and basic analysis using tools like Excel and SQL. Both roles are common in various industries, but Python Data Science roles typically involve more technical and coding skills, while Data Analyst roles focus on interpreting data for business insights.

What are popular job titles related to Entry Level Python Data Science jobs in Huntsville, AL? For Entry Level Python Data Science jobs in Huntsville, AL, the most frequently searched job titles are:
What job categories do people searching Entry Level Python Data Science jobs in Huntsville, AL look for? The top searched job categories for Entry Level Python Data Science jobs in Huntsville, AL are:
Entry Level Python Data Science jobs near you
Infographic showing various Entry Level Python Data Science job openings in Huntsville, AL as of June 2026, with employment types broken down into 3% Internship, 89% Full Time, and 8% Part Time. Highlights an 94% In-person, 3% Hybrid, and 3% Remote job distribution, with an average salary of $120,143 per year, or $57.8 per hour.

Machine Learning Engineer

Waypoint Human Capital

Huntsville, AL

Other

Posted 29 days ago

Job description

Position Title: Machine Learning Engineer
Position Type: Full-time, On-Site
Location: Huntsville, AL
Clearance: Active TS
Description:
Waypoint's client is seeking a Machine Learning Engineer to support mission-critical efforts within a secure environment at the Missile and Space Intelligence Center. This role focuses on developing, integrating, and operationalizing machine learning solutions that support advanced analytics and intelligence capabilities.
The selected candidate will work across the full machine learning lifecycle, from building data pipelines and training models to deploying and monitoring production systems. This position requires a strong blend of software engineering and data science expertise, with a focus on scalability, performance, and system integration.
Responsibilities:
Integrate machine learning systems into existing software architectures and enterprise platforms
Design, build, and optimize data pipelines to support model training and inference
Develop, test, and deploy machine learning models into production environments
Manage transition from prototype to production, including deployment pipelines and monitoring solutions
Monitor model performance, including handling model drift, rollback, and failure scenarios
Conduct experiments and testing to evaluate and improve model accuracy and performance
Write clean, maintainable, and testable code in Python and related technologies
Collaborate with cross-functional teams to integrate ML capabilities into mission systems
Utilize CI/CD pipelines and GitOps practices to support automated deployment and version control
Support development in Linux and Windows environments
Required:
Active TS clearance (with ability to obtain TS/SCI with CI Polygraph)
Bachelor's degree in Computer Science, Mathematics, Statistics, Physics, or related technical field
Minimum 12+ years of overall experience, including 1-3 years working with machine learning frameworks
Strong programming skills in Python
Experience with machine learning frameworks, libraries, and data modeling techniques
Solid understanding of the machine learning lifecycle
Experience working with SQL and NoSQL databases
Experience working in Linux and Windows environments
Familiarity with CI/CD pipelines and Agile development methodologies
Understanding of software design and system integration principles
Desired:
Active TS/SCI with CI Polygraph (desired)
Experience working with large-scale (petabyte-level) datasets
Experience supporting multi-INT analytics environments
Experience deploying, monitoring, and scaling machine learning models in production
Experience with tools such as Docker, Jupyter Notebooks, PostgreSQL, GitLab, and GitHub
Experience implementing GitOps workflows
Experience working in secure or classified environment

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