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Data Science Jobs in Louisiana (NOW HIRING)

The Mutual Group

Actuarial Data Engineer

Iowa, LA · On-site +1

$91K - $130K/yr

The Actuarial Data Engineer will partner with actuarial, data science, and finance teams to deliver reliable and well-structured data solutions that support analytical workflows and business insights.

Jobtailor

AI Data Scientist

Bossier City, LA · On-site

$120 - $160/hr

Architect scalable data pipelines and develop intuitive front‑end experiences using Next.js * Translate intricate technical and analytical insights into clear presentations for leadership

THE PRUITT GROUP LLC

Data Engineer

New Orleans, LA · On-site

$110K - $132K/yr

Collaborate with data scientists, analysts, and other stakeholders to address data needs, while maintaining thorough documentation of pipelines, models, and integration workflows. * Cloud ...

THE PRUITT GROUP LLC

Data Engineer

New Orleans, LA · On-site

$110K - $132K/yr

Collaborate with data scientists, analysts, and other stakeholders to address data needs, while maintaining thorough documentation of pipelines, models, and integration workflows. * Cloud ...

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All JobsData Science JobsData Science Jobs in Louisiana

Data Science information

See Louisiana salary details

$32.1K

$105K

$168K

How much do data science jobs pay per year?

As of Aug 18, 2026, the average yearly pay for data science in Louisiana is $104,957.00, according to ZipRecruiter salary data. Most workers in this role earn between $84,200.00 and $116,300.00 per year, depending on experience, location, and employer.

What is data science?

Data science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It combines skills from statistics, computer science, and domain expertise to analyze and interpret complex data sets. Data scientists work with large amounts of data to identify patterns, make predictions, and help organizations make data-driven decisions.

What does a data scientist do?

As a Data Scientist, you are qualified to work in such diverse fields as research and development, politics, advertising and marketing, technology, healthcare, government, and higher education as well as multiple others. In general, your duties and responsibilities will be to compile and analyze relevant statistics and turn those numbers into algorithms that reveal insights that can be used by other researchers in their areas of study. Data Science can reveal things like consumer buying habits or the likelihood of success for a course of action. Other duties might vary, depending on your unique field of specialty. Related areas in which a Data Scientist might wish to focus include work as a Data Analyst, Machine Learning Engineer, and Project Manager.

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

To thrive as a Data Scientist, you need a strong background in statistics, programming (often Python or R), and data analysis, usually supported by a degree in a quantitative field. Familiarity with machine learning libraries (like scikit-learn or TensorFlow), big data tools (such as Hadoop or Spark), and data visualization platforms is typically required. Critical thinking, problem-solving, and effective communication are vital soft skills for translating complex data insights into actionable business strategies. These skills and qualities are essential for extracting value from data, driving informed decisions, and effectively collaborating with multidisciplinary teams.

What are some common challenges faced by data scientists when working with real-world datasets?

Data scientists often encounter challenges such as missing or inconsistent data, unstructured formats, and noisy information in real-world datasets. Cleaning and preprocessing data to ensure its quality can be time-consuming but is critical for building accurate models. Additionally, data scientists may work closely with domain experts and other team members to better understand the data's context and ensure their analyses align with business objectives. Overcoming these challenges requires strong problem-solving skills and effective collaboration within cross-functional teams.

What is the difference between Data Science vs Data Analyst?

AspectData ScienceData Analyst
Required skillsStatistics, programming (Python, R), machine learningData visualization, SQL, basic statistics
Work environmentDeveloping models, predictive analytics, researchReporting, data cleaning, descriptive analysis
Tools usedPython, R, Jupyter, TensorFlowExcel, SQL, Tableau, Power BI
Industry usageTech, finance, healthcare, e-commerceRetail, marketing, finance, healthcare

Data Science and Data Analyst roles often overlap but differ mainly in scope. Data Scientists focus on building predictive models and advanced analytics, requiring programming and machine learning skills. Data Analysts primarily handle data cleaning, reporting, and visualization. Both roles are essential in data-driven industries, but Data Science is more technical and research-oriented, while Data Analysis emphasizes interpreting data for business insights.

Is a data scientist in high demand?

Data scientists are in high demand across many industries due to the increasing reliance on data-driven decision making. The role requires skills in programming, statistics, and machine learning, and job growth is expected to continue as organizations expand their data capabilities.

What jobs can a data scientist do?

A data scientist can work in roles such as data analyst, machine learning engineer, data engineer, or business intelligence analyst. These roles involve analyzing large datasets, developing predictive models, and using tools like Python, R, and SQL to support decision-making across various industries.
More about Data Science jobs

What are the most commonly searched types of Data Science jobs in Louisiana?

The most popular types of Data Science jobs in Louisiana are:

What are popular job titles related to Data Science jobs in Louisiana?

For Data Science jobs in Louisiana, the most frequently searched job titles are:

What job categories do people searching Data Science jobs in Louisiana look for?

The top searched job categories for Data Science jobs in Louisiana are:

What cities in Louisiana are hiring for Data Science jobs?

Cities in Louisiana with the most Data Science job openings:

Data Science jobs near you
Infographic showing various Data Science job openings in Louisiana as of August 2026, with employment types broken down into 6% Internship, 88% Full Time, and 6% Part Time. Highlights an 100% In-person job distribution, with an average salary of $104,957 per year, or $50.5 per hour.

Healthcare Data Analyst (On-Site in Lafayette, LA)

FMOLHS

Lafayette, LA

Full-time

Re-posted 14 days ago

Job description

This role will be located on-site in Lafayette, LA.

The Healthcare Data Analyst 2 plays a crucial role in analyzing and interpreting complex health data to help improve patient outcomes, operational efficiency, and overall healthcare delivery within our health system. The ideal candidate will have advanced analytical skills, a strong understanding of healthcare data, and the ability to work collaboratively with various stakeholders across the organization.

Education: Bachelor's degree in Health Informatics, Data Science, Statistics, Public Health, Business/Healthcare Administration or a related field.

Experience: Minimum of 3 years of experience in healthcare data analysis or a related field including internship.

Technical Skills: Proficiency in data analysis tools and software, such as SQL, SAS, R, Python, or Tableau. Strong understanding of electronic health records (EHR) systems, healthcare databases, and data standards (e.g., ICD-10, CPT codes).

Analytical Skills: Advanced analytical and problem-solving skills. Ability to interpret complex data sets and provide meaningful insights.

Communication Skills: Excellent verbal and written communication skills. Ability to present data findings clearly and concisely to non-technical stakeholders.

Attention to Detail: Strong attention to detail and commitment to data accuracy and quality.

Team Player: Ability to work effectively in a collaborative, team-oriented environment.

Data Analysis and Reporting: Perform advanced data analysis to support clinical and operational decision-making. Generate comprehensive reports and data visualizations to communicate findings effectively.

Data Management: Ensure the accuracy, integrity, and security of healthcare data. Manage data extraction, transformation, and loading (ETL) processes.

Performance Metrics: Develop and monitor key performance indicators (KPIs) to assess the effectiveness of clinical and operational processes. Provide actionable insights to improve performance.

Research and Evaluation: Conduct research and evaluation projects to assess the impact of healthcare interventions and programs. Collaborate with clinical and administrative teams to design and implement studies.

Compliance and Standards: Ensure compliance with relevant regulations, standards, and best practices in healthcare data management and analysis.

Collaboration: Work closely with IT, clinical, and administrative teams to identify data needs and provide analytical support. Participate in cross-functional projects and initiatives.
 

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