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Senior Data Analyst Machine Learning Jobs in Mobile, AL

Workiva

Spring 2027 Intern - Data Analytics

Mobile, AL · On-site

$22/hr

Analyze data to drive decision-making on new features of Wdesk * Suggest new product development ... your learning experience through additional internship seasons Why Join Workiva Workiva is the ...

Workiva

Spring 2027 Intern - Data Analytics

Mobile, AL · On-site +1

$22/hr

Analyze data to drive decision-making on new features of Wdesk * Suggest new product development ... your learning experience through additional internship seasons Why Join Workiva Workiva is the ...

22nd State Bank

Sr. Credit Analyst

Mobile, AL · On-site

Job Summary Senior Credit Analyst. The primary responsibility of this position is to help maintain ... data for new loans, loan extensions, loan modifications, and loan renewals. Critical features of ...

22nd State Bank

Sr. Credit Analyst

Mobile, AL · On-site

$70 - $90/hr

Job Summary Senior Credit Analyst. The primary responsibility of this position is to help maintain ... Analytical - Collects and researches data. * Problem Solving - Identifies and resolves problems in ...

22nd State Bank

Sr. Credit Analyst

Mobile, AL · On-site

Job Summary Senior Credit Analyst. The primary responsibility of this position is to help maintain ... data for new loans, loan extensions, loan modifications, and loan renewals. Critical features of ...

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All JobsData Analyst Machine Learning JobsSenior Data Analyst Machine Learning JobsSenior Data Analyst Machine Learning Jobs in Mobile, AL

Senior Data Analyst Machine Learning information

See Mobile, AL salary details

$54.6K

$98.5K

$134.5K

How much do senior data analyst machine learning jobs pay per year?

As of Sep 5, 2026, the average yearly pay for senior data analyst machine learning in Mobile, AL is $98,469.00, according to ZipRecruiter salary data. Most workers in this role earn between $85,300.00 and $107,700.00 per year, depending on experience, location, and employer.

What is a senior data analyst machine learning?

A Senior Data Analyst in Machine Learning is a professional who analyzes large datasets to extract insights and supports the development and implementation of machine learning models. They often work closely with data scientists, engineers, and business stakeholders to identify trends, prepare data, and ensure the quality and relevance of data used in machine learning projects. Their role typically includes advanced data analysis, developing data pipelines, creating reports, and interpreting the results of machine learning models to drive business decisions.

What are the key skills and qualifications needed to thrive as a senior data analyst machine learning?

To thrive as a Senior Data Analyst Machine Learning, you need strong analytical skills, expertise in statistics, and advanced proficiency in programming languages like Python or R, typically supported by a degree in a quantitative field. Familiarity with machine learning frameworks (such as scikit-learn, TensorFlow, or PyTorch), data visualization tools, and experience with SQL databases are essential, along with relevant certifications like Google Data Analytics or AWS Machine Learning. Outstanding problem-solving abilities, collaboration, and the capacity to communicate complex concepts clearly make individuals stand out in this role. These skills and qualities are crucial for extracting actionable insights from data, building effective predictive models, and driving data-driven decision-making within organizations.

How does a senior data analyst machine learning typically collaborate with data science and engineering teams?

As a Senior Data Analyst with a focus on Machine Learning, you'll work closely with both data science and engineering teams to bridge the gap between data insights and model deployment. You may be responsible for preparing and analyzing large datasets, communicating findings and business needs to data scientists, and ensuring that machine learning models are implemented effectively. Regular collaboration includes participating in code reviews, refining feature engineering, and translating technical results into actionable business recommendations. This cross-functional teamwork is key to ensuring that projects move smoothly from conception to production.

What is the difference between Senior Data Analyst Machine Learning vs Data Scientist?

AspectSenior Data Analyst Machine LearningData Scientist
Required CredentialsBachelor's degree in Data Science, Statistics, or related field; experience with machine learning toolsBachelor's or Master's in Data Science, Computer Science, or related; strong programming and statistical skills
Work EnvironmentData analysis teams, business units, focus on applying ML models to business problemsResearch and development teams, focus on model development, experimentation, and innovation
Employer & Industry UsageFinance, healthcare, retail, and tech companies using ML for insightsTech firms, startups, research institutions developing advanced models

While both roles involve working with data and machine learning, Senior Data Analyst Machine Learning typically focuses on applying existing models to solve business problems, whereas Data Scientists develop new models and algorithms, often engaging in more research and experimentation.

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Senior Data Analyst Machine Learning jobs near you

Data Scientist (Statistician) - Direct Hire

Criminal Investigation & Law Enforcement | IRS Careers

Mobile, AL • On-site

$125K/yr

Full-time

Posted 9 days ago

Internal Revenue Service rating

7.4

Company rating: 7.4 out of 10

Based on 130 frontline employees who took The Breakroom Quiz

158th of 297 rated public sector bodies

Job description

WHAT IS LARGE BUSINESS AND INTERNATIONALDIVISION?
A description of the business units can be found at: https://www.jobs.irs.gov/about/who/business-divisions

  • Position is to be filled in the following area(s):
    • LBI - ADCCI - Assistant Deputy Commissioner Compliance Integration.


REVIEW THE ADDITIONAL INFORMATION BELOW FOR FURTHER DETAILS

Qualifications:Federal experience is not required. Experience may have been gained in the public sector, private sector or through Volunteer Service. One year of experience refers to full-time work; part-timework is considered on a prorated basis. To ensure full credit for your work experience, please indicate dates of employment by month/day/year, and indicate number of hours worked per week, on your resume.
You must meet the following requirements by the closing date of this announcement.
QUALIFICATION REQUIREMENTS: To qualify for this position, you must meet the qualification requirements outlined below:
BASIC REQUIREMENTS (IOR) ALL GRADES:
EDUCATION: A degree that included 15 semester hours in statistics (or in mathematics and statistics, provided at least 6 semester hours were in statistics), and 9 additional semester hours in one or more of the following: physical or biological sciences, medicine, education, or engineering; or in the social sciences including demography, history, economics, social welfare, geography, international relations, social or cultural anthropology, health sociology, political science, public administration, psychology, etc. Credit toward meeting statistical course requirements should be given for courses in which 50 percent of the course content appears to be statistical methods, e.g., courses that included studies in research methods in psychology or economics such as tests and measurements or business cycles, or courses in methods of processing mass statistical data such as tabulating methods or electronic data processing.
OR
COMBINATION OF EDUCATION AND EXPERIENCE: Combination of education and experience includes courses as shown in A above, plus appropriate experience or additional education. The experience should have included a full range of professional statistical work such as (a) sampling, (b) collecting, computing, and analyzing statistical data, and (c) applying statistical techniques such as measurement of central tendency, dispersion, skewness, sampling error, simple and multiple correlation, analysis of variance, and tests of significance.
AND
SPECIALIZED EXPERIENCE GS-14: In addition to meeting basic requirements, to be eligible for this position at this grade level, you must have one (1) year of specialized experience at a level of difficulty and responsibility equivalent to the GS-13 grade level in the Federal service.
Specialized experience for this position includes:
  • Experience identifying and assessing the validity and reliability of relevant data sources and retrieving structured and unstructured data in multiple types and formats, including Extensible Markup Language (XML) files and large datasets, for use in data science projects.
  • Experience cleaning, transforming, combining, and integrating structured and unstructured data from multiple sources, including identifying and resolving missing values, outliers, and duplicate records, to prepare data for analysis.
  • Experience applying data-mining process models, including the Cross-Industry Standard Process for Data Mining (CRISP-DM) or Sample, Explore, Modify, Model, Assess (SEMMA), to collect, prepare, analyze, and evaluate data during data science projects.
  • Experience applying statistical methods, probability, statistical inference, hypothesis testing, experimental design, forecasting, and sampling methods to analyze data, evaluate results, and support program or business decisions.
  • Experience developing and evaluating analytical and artificial intelligence models using machine learning, text analytics, natural language processing, large language models, graph theory, link analysis, optimization models, complex adaptive systems, or deep-learning neural networks.
  • Experience using programming languages, query languages, data-intelligence platforms, and data-storage technologies, including R, Python, Structured Query Language (SQL), Java, Databricks, Sybase, Oracle, or open-source databases, to retrieve, process, query, analyze, and integrate data during data science projects.
  • Experience planning, coordinating, monitoring, and evaluating data science projects; reviewing technical deliverables for validity and reliability; and communicating analytical findings, model results, limitations, conclusions, and recommendations to technical and nontechnical stakeholders through written products, presentations, graphs, tables, charts, or business-intelligence products.

AND
You must also meet the following requirements:
  • MINIMUM AGE REQUIREMENT: Minimum age for federal employment is 18 years old, or at least 16 years old and have:
    • Graduated from high school or been awarded a certificate equivalent to graduating from high school; or
    • Completed a formal vocational training program; or
    • Received a statement from school authorities agreeing with your preference for employment rather than continuing your education

For more information on qualifications please refer to OPM's Qualifications Standards.Education:A college or university degree generally must be from an accredited (or pre-accredited) college or university recognized by the U.S. Department of Education. For a list of schools which meet these criteria, please refer to Department of Education Accreditation page.
FOREIGN EDUCATION: Education completed in foreign colleges or universities may be used to meet the requirements. You must show proof the education credentials have been deemed to be at least equivalent to that gained in conventional U.S. education program. It is your responsibility to provide such evidence when applying. Click here (Section 3, Explanation of Terms) or here for Foreign Education Credentialing instructions.
We recommend choosing an evaluator from a member organization of one of the following national associations of credential evaluation services: National Association of Credential Evaluation Services (NACES) or Association of International Credentials Evaluators (AICE).Employment Type: OTHER

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