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Remote Data Scientist Jobs in Hawaii (NOW HIRING)

SAIC

Data Operations Engineer Principal

Honolulu, HI · On-site +1

$120K - $160K/yr

None Potential for Remote Work: ORA_ON_SITE Description We are seeking a Data Operations Engineer ... Bachelor of Science degree in Computer Science, Mathematics, Electrical Engineering, Physics ...

Booz Allen Hamilton, Inc.

Data Engineer

Honolulu, HI · On-site +1

$113K - $135K/yr

Remote Work: No Job Number: R0244293 Location: Honolulu,HI,US Share job via: Share Data Engineer The Opportunity: Ever-expanding technology like IoT, machine learning, and artificial intelligence ...

Booz Allen Hamilton, Inc.

Data Engineer

Honolulu, HI · On-site +1

$113K - $135K/yr

Remote Work: No Job Number: R0240103 Location: Honolulu,HI,US Share job via: Share Data Engineer The Opportunity: Ever-expanding technology like IoT, machine learning, and artificial intelligence ...

SOSi

Virtual Environment Lead

Honolulu, HI · On-site +1

$149K - $197K/yr

Its capabilities include data science, software development, network engineering, intelligence ... Office environment with the possibility of remote work arrangements as per company policy. * Target ...

micro1 AI

Bioinformatics Software Engineer

Honolulu, HI · Remote

$80 - $110/hr

Remote micro1 is engaging Computational Biology & Cheminformatics Experts to contribute their ... Assess and review AI-generated outputs for scientific rigor, accuracy, and practical relevance ...

US Department of Commerce

Meteorologist

Honolulu, HI · On-site +1

$40K/yr

Degree in Meteorology, Atmospheric Science, or another natural science major that includes: * At ... Using current hydro-meteorological data to monitor conditions and assist with forecast preparation ...

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Remote Data Scientist information

See Hawaii salary details

$39K

$127.5K

$204.2K

How much do remote data scientist jobs pay per year?

As of Aug 30, 2026, the average yearly pay for remote data scientist in Hawaii is $127,520.00, according to ZipRecruiter salary data. Most workers in this role earn between $102,300.00 and $141,300.00 per year, depending on experience, location, and employer.

What is a remote data scientist?

Remote data scientists are professionals who analyze and interpret complex data while working outside of a traditional office environment, typically from home or another remote location. They use statistical methods, machine learning, and programming to extract insights from data, helping organizations make data-driven decisions. Remote data scientists collaborate with teams virtually, often using tools for communication, data analysis, and project management. This flexible work arrangement allows for talent from anywhere to contribute to companies worldwide, provided they have reliable internet and the necessary technical skills.

What does a remote data scientist do?

Remote data scientists collect, confirm, and interpret data to determine useful information for their employer. Unlike in-house data scientists, remote data scientists work outside the office, either from home or another location with Wi-Fi accessibility. Remote data scientists help organizations identify patterns and trends in their data to provide information about lucrative opportunities, necessary improvements, and potential innovations. The information they get from the records they gather helps businesses make decisions in critical areas, such as product development, sales and marketing techniques, and client retention. You find remote data scientists in many different industries, including pharmaceuticals, manufacturing, and banking.

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

To thrive as a Remote Data Scientist, you need strong analytical skills, proficiency in statistics, and a solid background in mathematics or computer science, usually demonstrated through a relevant degree. Familiarity with programming languages like Python or R, experience with machine learning frameworks, and knowledge of data visualization tools are typically required, along with certifications such as Microsoft Certified: Azure Data Scientist Associate or Google Professional Data Engineer. Excellent communication, problem-solving abilities, and self-motivation are critical soft skills for collaborating remotely and delivering insights to stakeholders. These skills are crucial for effectively analyzing data, building predictive models, and driving data-driven decisions in a distributed work environment.

How does a remote data scientist typically collaborate with team members across different time zones?

As a remote data scientist, effective collaboration across time zones often involves leveraging asynchronous communication tools like Slack, project management platforms, and version control systems such as Git. Regular virtual meetings are scheduled to accommodate overlapping hours, and clear documentation becomes crucial for keeping everyone aligned. Proactive communication, sharing progress updates, and setting clear expectations help ensure seamless teamwork despite geographical differences. This structure allows remote data scientists to contribute meaningfully while maintaining flexibility in their work schedules.

What is the difference between Remote Data Scientist vs Remote Data Analyst?

AspectRemote Data ScientistRemote Data Analyst
Required CredentialsDegree in Data Science, Statistics, or related field; often requires programming skills in Python or RDegree in Analytics, Business, or related field; may require proficiency in Excel, SQL, and visualization tools
Work EnvironmentResearch-focused, developing models, machine learning, and predictive analyticsData interpretation, reporting, and visualization to support business decisions
Employer & Industry UsageTech companies, finance, healthcare, and e-commerceRetail, marketing, finance, and consulting firms

Remote Data Scientists focus on building models and advanced analytics, while Remote Data Analysts interpret data and create reports. Both roles require strong analytical skills but differ in technical depth and project scope.

More about Remote Data Scientist jobs

What are the most commonly searched types of Data Scientist jobs in Hawaii?

The most popular types of Data Scientist jobs in Hawaii are:

What are popular job titles related to Remote Data Scientist jobs in Hawaii?

For Remote Data Scientist jobs in Hawaii, the most frequently searched job titles are:

What job categories do people searching Remote Data Scientist jobs in Hawaii look for?

The top searched job categories for Remote Data Scientist jobs in Hawaii are:

What cities in Hawaii are hiring for Remote Data Scientist jobs?

Cities in Hawaii with the most Remote Data Scientist job openings:

Remote Data Scientist jobs near you
Infographic showing various Remote Data Scientist job openings in Hawaii as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 14% Part Time, and 3% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $127,520 per year, or $61.3 per hour.

Data Operations Engineer Principal

Honolulu, HI • On-site, Remote

SAIC
IT Services • 10K+ employees

7.9

Company rating: 7.9 out of 10

Based on 79 frontline employees who took The Breakroom Quiz

80th of 226 rated it services

People enjoy working here

Good employer

Recommended by students

Learn more about SAICexternal

$120K - $160K/yr

Full-time

Posted 23 days ago

Job description

Job ID: 2615413

Location: Honolulu, HI, US

Date Posted: 2026-08-11

Category: Information Technology

Subcategory: Big Data Engineer

Schedule: Full-Time

Shift: Day Job

Travel: Yes - 10% of the time

Minimum Clearance Required: TS.SCI

Clearance Level Must Be Able to Obtain: None

Potential for Remote Work: ORA_ON_SITE


Description

We are seeking a Data Operations Engineer to design, build, and maintain real-time and near-realtime data ingestion pipelines supporting a mission-critical system and moves data across security domains using cross domain solutions (CDS) / guards. You will be responsible for the reliable flow of streaming data from a wide range of sources into our data platform, ensuring data quality, observability, and scalability.

You'll partner closely with data engineers, platform engineers, analytics teams, and security/governance personnel to deliver trustworthy, low-latency data that supports operational decisions — while ensuring compliance with cross-domain and data classification requirements. This position is on-site in Honolulu, HI.

Key Responsibilities

  • Aid the team in delivering continual data feeds to users and monitoring the health of data quality and overall data ingest.
  • Design, build, and maintain resilient real-time/near-realtime ingestion pipelines using Apache NiFi and REST APIs, with appropriate backpressure, prioritization, retries, and error-handling strategies.
  • Configure and monitor data flows moving across security domains via cross domain guards/solutions, ensuring data integrity and compliance with transfer policies.
  • Parse, transform, validate, and route structured and unstructured data in a variety of formats, including Excel, CSV, JSON, and XML.
  • Employ data manipulation and visualization tools (e.g., Grafana, Prometheus) to effectively convey pipeline status, data quality, and historical trends to leadership, users, and the data team.
  • Collaborate with platform, software, and other data engineers to (re)configure and continuously improve data ingestion pipeline reliability.
  • Develop and maintain software/scripts to automate monitoring of real-time feeds and alert on timeliness, volume, lineage, and distribution data issues.
  • Translate learnings from historical pipeline data into actionable steps to improve data ingest reliability and performance.
  • Partner with security and governance teams to enforce encryption, authentication, authorization, and data classification requirements across pipelines and cross-domain transfers.
  • Write and maintain scripts (Python, Bash, or similar) to automate data processing, validation, and monitoring tasks.
  • Document data flows, system configurations, and standard operating procedures.

Qualifications

  • TYPICAL EDUCATION AND EXPERIENCE: Bachelors and nine (9) years or more experience; Masters and seven (7) years or more experience ; PhD or JD and four (4) years or more experience.
  • U.S. Citizenship and an active TS/SCI clearance.
  • Bachelor of Science degree in Computer Science, Mathematics, Electrical Engineering, Physics, Information Systems, Information Technology, or related field.
  • 3+ years of experience in data engineering, data operations, or DevOps roles supporting production data pipelines.
  • Proficiency in Python, Bash, or similar scripting languages commonly used in data science/data analytics applications.
  • Working knowledge of the Linux (RedHat) command line; familiarity with Windows environments.
  • Solid understanding of data engineering fundamentals: data pipelines, streaming architectures, ETL/ELT concepts, and data quality principles.
  • Familiarity with JSON, XML, CSV, and Excel data formats, parsing, and transformation.

Preferred Qualifications (Nice to Have)

  • Experience with streaming/messaging platforms and tools: Kafka, JMS, Apache Flink/Spark Streaming.
  • Experience with monitoring/observability tools: Grafana, Prometheus, Elasticsearch.
  • Experience with data platforms such as Snowflake.
  • Familiarity with cross domain solutions/guards (e.g., data diode concepts, transfer validation, content filtering).
  • Experience enforcing encryption, authentication/authorization, and data classification policies in partnership with security/governance teams.
  • Experience with version control tools (e.g., Git) and Agile/Scrum practices.
  • Prior experience in a government, defense, or intelligence community environment.

Target salary range: $120,001 - $160,000. The estimate displayed represents the typical salary range for this position based on experience and other factors.

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