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Data Scientist Machine Learning Jobs in Alabama (NOW HIRING)

... preparing students for data science roles and advanced AI coursework. * Conceptual Teaching ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

... preparing students for data science roles and advanced AI coursework. * Conceptual Teaching ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

... preparing students for data science roles and advanced AI coursework. * Conceptual Teaching ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

... preparing students for data science roles and advanced AI coursework. * Conceptual Teaching ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Senior Data Scientist/Data Engineer Full-time Huntsville, AL About Us Trideum Corporation is a 100 ... Familiarity with building artificial intelligence (AI) or machine learning pipelines for advanced ...

Collaborate with data scientists to develop, train, and evaluate machine learning models. * Build and maintain MLOps pipelines, including data ingestion, feature engineering, model training ...

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Data Scientist Machine Learning information

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$34K

$111.2K

$178.1K

How much do data scientist machine learning jobs pay per year?

As of Aug 13, 2026, the average yearly pay for data scientist machine learning in Alabama is $111,249.00, according to ZipRecruiter salary data. Most workers in this role earn between $89,300.00 and $123,300.00 per year, depending on experience, location, and employer.

What is a data scientist machine learning?

A Data Scientist specializing in Machine Learning (ML) uses statistical methods, algorithms, and computational power to analyze data and create predictive models. They work with large datasets to identify patterns, train machine learning models, and improve decision-making processes. Responsibilities often include data cleaning, feature engineering, model selection, and performance evaluation. They may collaborate with engineers and business teams to deploy models in real-world applications. Strong skills in programming (Python, R), ML frameworks (TensorFlow, Scikit-learn), and data visualization are essential.

What are the key skills and qualifications needed to thrive in the data scientist machine learning position, and why are they important?

To excel as a Data Scientist Machine Learning, you need a strong proficiency in statistics, programming (typically Python or R), and a solid understanding of machine learning algorithms, usually backed by a degree in computer science, mathematics, or a related field. Familiarity with tools such as TensorFlow, scikit-learn, SQL databases, and cloud platforms, as well as certifications in data science or machine learning, is commonly expected. Analytical thinking, problem-solving skills, and effective communication are vital soft skills in this profession. These qualifications combine to drive impactful insights and enable the successful development and deployment of machine learning models in business environments.

What are the typical day-to-day responsibilities of a data scientist machine learning?

On a typical day, a Data Scientist specializing in Machine Learning might gather and preprocess data, design and implement machine learning models, and evaluate their performance to solve real-world problems. They often collaborate with data engineers, software developers, and business stakeholders to translate business objectives into technical solutions and integrate models into existing systems. Other responsibilities can include visualizing data insights, conducting experiments to tune algorithms, and staying current with new developments in the field. The work is highly collaborative and iterative, requiring clear communication with various teams to ensure project goals are met efficiently.

What are the most commonly searched types of Data Scientist Machine Learning jobs in Alabama?

The most popular types of Data Scientist Machine Learning jobs in Alabama are:

What are popular job titles related to Data Scientist Machine Learning jobs in Alabama?

For Data Scientist Machine Learning jobs in Alabama, the most frequently searched job titles are:

Infographic showing various Data Scientist Machine Learning job openings in Alabama as of August 2026, with employment types broken down into 79% Full Time, and 21% Contract. Highlights an 85% In-person, 10% Hybrid, and 5% Remote job distribution, with an average salary of $111,249 per year, or $53.5 per hour.

DATA SCIENTIST (CYBER/CLOUD)

Quantum Research International

Huntsville, AL • On-site

$90 - $120/hr

Other

Posted 20 days ago


Job description

If you are unable to complete this application due to a disability, contact this employer to ask for an accommodation or an alternative application process.

DATA SCIENTIST (CYBER/CLOUD)

Full Time HUNTSVILLE, AL, HUNTSVILLE, AL, US

18 days ago Requisition ID: 1459

Job Description

Edit

Job Posting Title DATA SCIENTIST (CYBER/CLOUD)

Job Description

Overview:

Quantum Research International, Inc. (Quantum) provides our national defense and federal civilian and industry customers with services and products in the following main areas: 1) Cybersecurity and Information Operations; 2) Space Operations and Control; 3) Aviation Systems; 4) Ground, Air and Missile Defense, and Fires Support Systems; 5) Intelligence Programs Support; 6) Experimentation and Test; 7) Program Management; and (8) Audio/Visual Technology Applications. Quantum's Corporate Office is located in Huntsville, AL, but Quantum actively hires for positions nationwide and internationally. We pride ourselves on providing high quality support to the U.S. Government and our Nation's Warfighters. In addition to our corporate office, we have physical locations in Aberdeen, MD; Colorado Springs, CO; Crestview, FL, Orlando, FL, and Tupelo, MS

Mission:

Quantum Research Int is seeking a motivated Mid-Level (or Junior) Data Scientist to support advanced AI/ML and large language model (LLM) initiatives within a cybersecurity and risk analysis platform. You will contribute to the development, deployment, and integration of predictive models and LLM-based solutions to process structured and unstructured data (including SBOMs, CVEs, vulnerability data, and critical program information), generate actionable insights, and support data-informed decision making in a high-stakes defense environment.

Responsibilities:

  • Support the formulation, design, and execution of data-driven projects focused on cybersecurity risk prioritization, vulnerability analysis, exposure trends, anomaly detection, and automated risk reporting.
  • Develop, fine-tune, and assist in deploying AI/ML models and LLM-based solutions (e.g., for natural language understanding, summarization, and insight generation from unstructured sources such as SBOMs and technical documentation).
  • Help integrate model outputs into data pipelines, dashboards, and visualization tools to deliver clear insights for technical and non-technical stakeholders.
  • Collaborate with data analysts, software developers, and cross-functional teams to embed predictive analytics and LLM capabilities into existing platforms.
  • Clean, collate, and preprocess complex datasets from multiple sources; assess and improve data quality.
  • Apply statistical methods, machine learning algorithms, and LLM techniques to extract actionable insights under guidance.
  • Perform model validation, performance monitoring, and testing.
  • Optimize models for accuracy, scalability, and usability.
  • Produce and present reports, summaries, and visualizations that communicate findings and implications.
  • Stay current with advancements in AI/ML, LLMs, and cybersecurity data domains.
  • Contribute to MLOps practices including model versioning and deployment support.
  • Assist in LLM-driven techniques for assessing compromised datasets to gauge impact and score risk.

Requirements:

  • 2–5+ years of professional experience as a Data Scientist with emphasis on AI/ML model development and deployment (0–3 years for Junior level).
  • Bachelor's degree required; Master's preferred in Data Science, Computer Science, Statistics, Mathematics, or a closely related field.
  • Proficiency in Python (primary); experience with R is a plus.
  • Hands-on experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn) and basic LLM implementation/fine-tuning (BERT, GPT-style models, LLaMA/Ollama, or equivalents).
  • Strong proficiency in data manipulation and analysis (Pandas, NumPy) and SQL.
  • Ability to translate technical outputs into clear, actionable insights and presentations.
  • Familiarity with cybersecurity data types and concepts (CVEs, SBOMs, vulnerability severity scoring, risk exposure metrics) or enterprise risk management domains is beneficial.
  • Active TS//SCI security clearance (or ability to obtain and maintain).
  • Preferred certs: CISSP, CySA+, Security+, CEH

Desired/Preferred Skills:

  • Prior experience in cybersecurity, defense, national security, or enterprise risk management environments.
  • Experience integrating AI/ML or LLM solutions with cloud platforms (AWS SageMaker, Azure ML, GCP) or on-premises systems.
  • Knowledge of basic MLOps tools and practices.
  • Experience with business intelligence and dashboard tools (Tableau, Power BI).
  • Familiarity with big data frameworks or large-scale data processing.
  • Additional programming skills (Java, C++) are a plus.
  • Strong foundation in statistics, linear algebra, and experimental design.
  • This role combines technical AI/ML and LLM contributions with collaboration in a sensitive, cleared environment. Ideal candidates are proactive, eager to learn, and comfortable applying advanced modeling to real-world cybersecurity challenges.

#LI Onsite #LI-JL1

Equal Opportunity Employer/Affirmative Action Employer M/F/D/V: All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, disability, veteran status, genetic information, sexual orientation, gender identity, or any other characteristic protected by law. *Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

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