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At Home Data Scientist Risk Jobs in Gainesville, FL

Data Security and Privacy Statement At Help at Home, we prioritize protecting your personal information during the hiring process. We comply with all relevant data privacy regulations, including ...

Data Security and Privacy Statement At Help at Home, we prioritize protecting your personal information during the hiring process. We comply with all relevant data privacy regulations, including ...

Data Security and Privacy Statement At Help at Home, we prioritize protecting your personal information during the hiring process. We comply with all relevant data privacy regulations, including ...

Data Science Tutor

Gainesville, FL · Remote

$18 - $40/hr

All from the comfort of your home. Why Join Our Platform? * Earn incrementally higher pay for each ... Skilled at teaching the full data science workflow from question formulation through insight ...

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At Home Data Scientist Risk information

See Gainesville, FL salary details

$34K

$111.2K

$178K

How much do at home data scientist risk jobs pay per year?

As of Aug 29, 2026, the average yearly pay for at home data scientist risk in Gainesville, FL is $111,202.00, according to ZipRecruiter salary data. Most workers in this role earn between $89,200.00 and $123,200.00 per year, depending on experience, location, and employer.

What is the difference between At Home Data Scientist Risk vs At Home Data Analyst Risk?

AspectAt Home Data Scientist RiskAt Home Data Analyst Risk
Required CredentialsTypically requires a master's or Ph.D. in data science, statistics, or related fieldsUsually requires a bachelor's degree in data analysis, statistics, or related areas
Work EnvironmentRemote, often involves complex modeling and predictive analyticsRemote, focuses on data interpretation and reporting
Employer & Industry UsageUsed in tech, finance, healthcare for advanced analyticsCommon in retail, marketing, and business sectors for reporting

The main difference between At Home Data Scientist Risk and At Home Data Analyst Risk lies in the complexity of tasks and required credentials. Data Scientists typically handle advanced modeling and require higher education, while Data Analysts focus on data reporting and analysis with more accessible qualifications. Both roles are remote and industry-specific, but Data Scientists often work on predictive analytics, whereas Data Analysts interpret existing data for decision-making.

What are popular job titles related to At Home Data Scientist Risk jobs in Gainesville, FL?

For At Home Data Scientist Risk jobs in Gainesville, FL, the most frequently searched job titles are:

What job categories do people searching At Home Data Scientist Risk jobs in Gainesville, FL look for?

The top searched job categories for At Home Data Scientist Risk jobs in Gainesville, FL are:

What cities near Gainesville, FL are hiring for At Home Data Scientist Risk jobs?

Cities near Gainesville, FL with the most At Home Data Scientist Risk job openings:

Data Scientist (Statistician)

US Department of the Treasury

Gainesville, FL • On-site

$125K/yr

Full-time

Posted 3 days ago

New


U.S. Department Of The Treasury rating

8.2

Company rating: 8.2 out of 10

Based on 13 frontline employees who took The Breakroom Quiz

309th of 851 rated public administrative organizations


Job description

WHAT IS LARGE BUSINESS AND INTERNATIONAL?

A description of the business units can be found at: https://www.jobs.irs.gov/about/who/business-divisions
  • Position(s) are to be filled in following area(s):
    • LBI - ADCCI - Compliance Planning & Analytics (CP&A), Workload Development & Delivery (WDD). Team will be determined at time of selection.

REVIEW THE ADDITIONAL INFORMATION BELOW FOR FURTHER DETAILSQualifications:

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.
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 requirement(s):

  • TIME AFTER COMPETITIVE APPOINTMENT (TACA): By the closing date (or if this is an open continuous announcement, by the cut-off date) specified in this job announcement, current civilian employees must have completed at least 90 days of federal civilian service since their latest non-temporary appointment from a competitive referral certificate, known as time after competitive appointment. For this requirement, a competitive appointment is one where you applied to and were appointed from an announcement open to "All US Citizens"
  • TIME IN GRADE (TIG): For positions above the GS-05,applicants must meet applicable time-in-grade requirements to be considered eligible. One year (52 weeks) at the next lower grade level is required to meet the time-in-grade requirements for the grade you are applying for. For positions at the GS-05, you cannot advance to the GS-05 if you have held a GS-02 in the past 52 weeks. There is no TIG restriction for GS-02, 03 or 04 positions.


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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