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Mining Data Analytics Jobs in Hawaii (NOW HIRING)

... analysis (e.g., variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g., data cleaning and transformation), data mining, data modeling ...

... analysis, data management, data mining, data modeling and assessment, artificial intelligence, and/or software engineering. • Active TS/SCI w/poly Preferred : • Foundations: (Mathematical ...

Data Scientist 2

Honolulu, HI · On-site

$98K - $108K/yr

... analysis (e.g., variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g., data cleaning and transformation), data mining, data modeling ...

Data Scientist 2

Honolulu, HI · On-site

$98K - $108K/yr

... analysis (e.g., variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g., data cleaning and transformation), data mining, data modeling ...

Data Engineer

Honolulu, HI · On-site

$113K - $135K/yr

Experience should demonstrate competency in key concepts from software engineering, computer programming, statistical analysis, data mining algorithms, machine learning, and modeling sufficient to ...

Data Engineer

Honolulu, HI · On-site

$113K - $135K/yr

Experience should demonstrate competency in key concepts from software engineering, computer programming, statistical analysis, data mining algorithms, machine learning, and modeling sufficient to ...

Data Engineer with Security Clearance

Honolulu, HI · On-site

$113K - $135K/yr

Experience should demonstrate competency in key concepts from software engineering, computer programming, statistical analysis, data mining algorithms, machine learning, and modeling sufficient to ...

Perform complex statistical analysis, modeling, and data mining to extract actionable insights from diverse intelligence datasets. * Design, develop, and implement advanced data visualizations to ...

New

Data Scientist - Sr.

Pearl City, HI · On-site

$90 - $130/hr

Perform complex statistical analysis, modeling, and data mining to extract actionable insights from diverse intelligence datasets. * Design, develop, and implement advanced data visualizations to ...

New

Data Engineer II

Honolulu, HI · On-site

$110K - $132K/yr

Data Engineers will partner closely with Analytics Engineers, who focus on downstream activities ... Process Mining & Stakeholder Collaboration * Experience with process mining and collaborating with ...

Data Engineer II

Honolulu, HI

$113K - $135K/yr

Data Engineers will partner closely with Analytics Engineers, who focus on downstream activities ... Process Mining & Stakeholder Collaboration * Experience with process mining and collaborating with ...

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Mining Data Analytics information

What does a mining data analytics do?

A mining data analyst collects, processes, and interprets data related to mining operations to improve efficiency, safety, and productivity. They use tools like SQL, Python, or specialized mining software to analyze large datasets and support decision-making. Strong analytical skills and knowledge of mining processes are essential for this role.

What is data analytics in the mining industry?

Data analytics in mining involves collecting and analyzing large datasets from exploration, production, and safety operations to optimize processes, improve efficiency, and reduce costs. Mining data analysts use tools like statistical software and machine learning to interpret sensor data, geological information, and operational metrics, supporting decision-making and resource management.

What is the difference between Mining Data Analytics vs Data Analyst?

AspectMining Data AnalyticsData Analyst
Required CredentialsBachelor's in Data Science, Mining Engineering, or related fields; certifications in data analytics or mining softwareBachelor's in Statistics, Data Science, or related fields; certifications in data analysis tools
Work EnvironmentMining sites, data centers, or corporate offices; focus on mineral extraction dataOffice settings, corporate or consulting firms; focus on business data
Employer & Industry UsageMining companies, resource extraction industriesVarious industries including finance, healthcare, retail

Mining Data Analytics and Data Analysts both analyze data, but Mining Data Analytics specializes in mineral extraction data within the mining industry, often requiring industry-specific knowledge and certifications. Data Analysts have a broader scope across multiple industries, focusing on business insights. While both roles involve data interpretation, their environments and applications differ significantly.

What are popular job titles related to Mining Data Analytics jobs in Hawaii? For Mining Data Analytics jobs in Hawaii, the most frequently searched job titles are:
What cities in Hawaii are hiring for Mining Data Analytics jobs? Cities in Hawaii with the most Mining Data Analytics job openings:
Infographic showing various Mining Data Analytics job openings in Hawaii as of July 2026, with employment types broken down into 1% As Needed, 77% Full Time, 19% Part Time, 1% Temporary, and 2% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution.

Full-time

Re-posted 25 days ago


Job description

What You'll be Owning:

  • We are actively searching for Data Scientists, located in Hawaii, to support our team. We have varying levels of Data Scientist roles, depending on years of experience and education.
  • Performs tasks associated with Big Data Platform management, utilizes skills in programming languages, develops prototype algorithms as well as algorithm refinements, and supports data visualization and analytics.  

What You Must Have:

  • Bachelor's Degree with 3 years of relevant experience OR Associates degree with 5 years of relevant experience 
  • Bachelor'sDegree must be in Mathematics, Applied Mathematics Statistics, Applied Statistics, Machine learning, Data Science, Operations Research, or Computer Science or a degree in a related field (Computer Information Systems, Engineering), a degree in the physical/hard sciences (e.g. physics, chemistry, biology, astronomy), or other science disciplines with a substantial computational component (i.e. behavioral, social, or life) may be considered if it included a concentration of coursework (5 or more courses) in advanced Mathematics (typically 300 level or higher, such as linear algebra, probability and statistics, machine learning)  and/or computer science (e.g. algorithms, programming, , data structures, data mining, artificial intelligence).  College-level requirements, or upper-level math courses designated as elementary or basic do not count.  Note: A broader range of degrees will be considered if accompanied by a Certificate in Data Science from an accredited college/university.
  • Relevant experience must be in designing/implementing machine learning, data science, advanced analytical algorithms, programming (skill in at least one high-level language (e.g., Python)), statistical analysis (e.g., variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g., data cleaning and transformation), data mining, data modeling and assessment, artificial intelligence, and/or software engineering. Experience in more than one area is strongly preferred
  • Active TS/SCI w/poly

What Would Be Nice to Have:

  • Foundations: (Mathematical, Computational, Statistical) 2. Data Processing: (Data management and curation, data description and visualization, workflow, and reproducibility)
  • Modeling, Inference, and Prediction: (Data modeling and assessment, domain-specific considerations)
  • Devise strategies for extracting meaning and value from large datasets. Make and communicate principled conclusions from data using elements of mathematics,
  • Statistics, computer science, and application specific knowledge.
  • Through analytic modeling, statistical analysis, programming, and/or another appropriate scientific method, develop and implement qualitative and quantitative methods for characterizing, exploring, and assessing large datasets in various states of organization, cleanliness, and structure that account for the unique features and limitations inherent in data holdings.
  • Translate practical mission needs and analytic questions related to large datasets into technical requirements and, conversely, assist others with drawing appropriate conclusions from the analysis of such data. Effectively communicate complex technical information to non-technical audiences. Make informed recommendations regarding competing technical solutions by maintaining awareness of the constantly shifting, processing, storage and analytic capabilities and limitations.