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Remote Data Science Jobs in Spring, TX (NOW HIRING)

Vitol

Software Engineer in Data Science

Houston, TX · On-site +1

$109.30K - $131.30K/yr

Act as a local champion for data science and AI, helping users adopt tools and articulate their changes and requirements to the wider team. The individual will work both with our data scientists and ...

Vitol

Software Engineer in Data Science

Houston, TX · On-site +1

$109.30K - $131.30K/yr

Act as a local champion for data science and AI, helping users adopt tools and articulate their changes and requirements to the wider team. The individual will work both with our data scientists and ...

Parkhill

Senior Data Scientist

Houston, TX · On-site +1

Contribute to the growth of Parkhill's data science capabilities. * Stay current with emerging trends in AI, machine learning, and data science applicable to the AEC and consulting sectors.

Relativity

Senior Applied Scientist

Houston, TX · On-site +1

Posting Type Remote/Hybrid Job Overview WHO WE ARE Relativity is a leading legal data intelligence ... Applied Science Team The Applied Science team operates at the core of Relativity's AI development.

Wesco

Director - Master Data Management

Houston, TX · Remote

Bachelors Degree - Computer Science, Mathematics, Statistics, Data Science, Electrical/Computer ... Ability to travel up to 25% #LI-JB1 #LI-REMOTE This amount is what we reasonably believe we will ...

Welo Data

Online Data Analyst - Remote

Conroe, TX · Remote

$14.50/hr

OVERVIEW Welo Data is looking for English speakers to join a remote project as a Search Quality Rater. In this role, you will help improve how search engines understand and deliver useful results to ...

New

Welo Data

Online Data Analyst - Remote

Pasadena, TX · Remote

$14.50/hr

OVERVIEW Welo Data is looking for English speakers to join a remote project as a Search Quality Rater. In this role, you will help improve how search engines understand and deliver useful results to ...

New

Welo Data

Online Data Analyst - Remote

Houston, TX · Remote

$14.50/hr

OVERVIEW Welo Data is looking for English speakers to join a remote project as a Search Quality Rater. In this role, you will help improve how search engines understand and deliver useful results to ...

New

Aquent

Marketing Writer

Houston, TX · On-site +1

$35 - $50/hr

May 11, 2026 Our energy data science consulting client is looking for a product marketing writer ... remote (US only) #LI-ES1

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Remote Data Science information

What Are the Qualifications to Get a Remote Data Science Job?

The qualifications for a remote data scientist depend in large part on your employer and their industry. Most employers expect remote data science professionals to have at least a bachelor’s degree in statistics, math, computer science, or a related field. Some expect postgraduate degrees in a field like data mining or machine learning or demonstrable skills in these areas. As a remote worker, you need access to relevant programs and an internet connection. You may also want to pursue certification, such as becoming a Certified Analytics Professional (CAP).

What are the key skills and qualifications 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, often supported by a relevant degree. Expertise in programming languages such as Python or R, familiarity with machine learning libraries, and experience with cloud-based data platforms are typically required. Excellent communication, self-motivation, and time management skills help you effectively collaborate and deliver results in a remote environment. These skills ensure accurate data analysis, meaningful insights, and successful teamwork despite physical distance.

How do remote data scientists typically collaborate with cross-functional teams to deliver insights?

Remote data scientists often work closely with product managers, engineers, and business analysts using digital collaboration tools such as Slack, Zoom, and project management platforms. Regular virtual meetings, code sharing via Git repositories, and clear documentation are essential to ensure alignment and transparency. While working remotely can present challenges in communication, proactive updates and scheduled syncs help foster strong teamwork and keep projects on track.

What is remote data science?

Remote data science refers to the practice of performing data analysis, modeling, and interpretation tasks from a location outside of a traditional office, such as from home or a co-working space. Remote data scientists use tools like Python, R, and SQL to analyze data, build predictive models, and communicate insights to stakeholders, all while collaborating virtually with their teams. This setup offers flexibility and can increase access to global job opportunities, but also requires strong self-motivation and communication skills to be effective.

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

AspectRemote Data ScienceRemote Data Analyst
Required CredentialsDegree in Data Science, Statistics, or related field; programming skills in Python/R; knowledge of machine learningDegree in Statistics, Mathematics, or related field; proficiency in Excel, SQL, and data visualization tools
Work EnvironmentCollaborative teams, research-focused, often involves building models and algorithmsData reporting, visualization, and interpreting data trends for decision-making
Employer & Industry UsageTech companies, finance, healthcare, e-commerceMarketing agencies, retail, finance, healthcare

Remote Data Science involves developing predictive models and advanced analytics, requiring programming and machine learning skills. Remote Data Analysts focus on interpreting data, creating reports, and visualizations. While both roles analyze data remotely, Data Scientists typically handle more complex modeling tasks, whereas Data Analysts focus on data interpretation and reporting.

More about Remote Data Science jobs
What are the most commonly searched types of Data Science jobs in Spring, TX? The most popular types of Data Science jobs in Spring, TX are:
What are popular job titles related to Remote Data Science jobs in Spring, TX? For Remote Data Science jobs in Spring, TX, the most frequently searched job titles are:
What job categories do people searching Remote Data Science jobs in Spring, TX look for? The top searched job categories for Remote Data Science jobs in Spring, TX are:
What cities near Spring, TX are hiring for Remote Data Science jobs? Cities near Spring, TX with the most Remote Data Science job openings:
Remote Data Science jobs near you
Infographic showing various Remote Data Science job openings in Spring, TX as of May 2026, with employment types broken down into 73% Full Time, 23% Part Time, and 4% Contract. Highlights an 58% Physical, 2% Hybrid, and 40% Remote job distribution.
Data Scientist - Remote

Data Scientist - Remote

NAVA Software Solutions

Houston, TX • On-site, Remote

Full-time

Posted 26 days ago

Job description

NAVA Software solutions is looking for a Data Scientist
Details:
Data Scientist
Location: Houston TX - Remote is ok
Duration: 12 months
Clients want a data scientist who can develop machine learning models to run forecast scenarios. Also, they want this person to be knowledgeable in AWS Cloud technology
A Data Scientist with physical pipeline experience typically specializes in analyzing and optimizing physical infrastructure pipelines, such as those used in the oil and gas industry or transportation networks. Here are some common job duties associated with this role:
  • Data collection and integration: Data scientists with physical pipeline experience gather data from various sources related to the infrastructure pipelines, such as sensors, SCADA (Supervisory Control and Data Acquisition) systems, or IoT devices. They integrate and consolidate the data for analysis and modeling.
  • Pipeline performance analysis: These professionals analyze the performance of physical pipelines by examining data related to flow rates, pressure levels, temperature, corrosion, and other relevant factors. They use statistical techniques and machine learning algorithms to identify patterns, anomalies, and potential issues that may affect pipeline operations.
  • Predictive modeling and maintenance optimization: Data scientists develop predictive models to forecast pipeline performance and detect potential failures or maintenance needs. They utilize historical data, sensor measurements, and other relevant parameters to train models that can predict future events, such as leaks, blockages, or equipment failures. By identifying critical maintenance requirements in advance, they can optimize maintenance schedules and minimize downtime.
  • Risk assessment and mitigation: Data scientists assess risks associated with physical pipelines, such as environmental hazards, security threats, or regulatory compliance. They develop risk assessment models and analyze the impact of different factors on pipeline safety and integrity. Based on these analyses, they propose mitigation strategies to minimize risks and ensure compliance with safety regulations.
  • Optimization of pipeline operations: Data scientists work on optimizing the operational efficiency of physical pipelines. They analyze data to identify areas of improvement, such as reducing energy consumption, optimizing transportation routes, or improving overall system performance. By applying data-driven approaches and algorithms, they provide recommendations to optimize pipeline operations and maximize efficiency.
  • Visualization and reporting: Data scientists with physical pipeline experience create visualizations, reports, and dashboards to communicate their findings and recommendations effectively. They present complex data in a visually understandable format, allowing stakeholders to make informed decisions regarding pipeline maintenance, operations, and risk management.
  • Collaboration with cross-functional teams: These professionals collaborate with engineers, domain experts, operations personnel, and other stakeholders involved in managing physical pipelines. They work together to understand the specific requirements, constraints, and challenges associated with the infrastructure. Effective communication and teamwork are essential to ensure alignment and successful implementation of data-driven solutions.
  • Continuous improvement and innovation: Data scientists keep up with the latest advancements in data science, machine learning, and pipeline technologies. They explore new methodologies, algorithms, and tools to enhance their skills and propose innovative solutions to address pipeline-related challenges.
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About NAVA Software Solutions

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NAVA is a strategic partner for companies seeking to develop or customize software and products. Our team of experts leverages cutting-edge technology and deep industry knowledge to provide customized solutions that drive business success. Whether you're looking to improve your operations, increase efficiency, or bring a new product to market, NAVA has the expertise and resources to help you achieve your goals. Trust us to be your partner in software and product development.

Industry

It services

Company size

51 - 200 Employees

Headquarters location

Rocky Hill, CT, US

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