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Remote Amazon Data Science Jobs in Conroe, TX (NOW HIRING)

Arva Intelligence

Data Engineer

Houston, TX · On-site +1

$95K - $130K/yr

Lead Modeling Scientist Location : Remote Base Salary Range: $95k - $130k General Position Description The Data Engineer is responsible for building and scaling the data and computational backbone ...

Insolation Technology Ltd

Data Analyst

Houston, TX · On-site +1

$21 - $26/hr

Bachelors degree in Data Science, Statistics, Mathematics, Engineering, or a related field ... Flexible work schedule and remote work options Job Type: * Full time Pay: * $21.00 - $26.00 per ...

Arva Intelligence

AI Software Engineer

Houston, TX · Remote

$115K - $145K/yr

Remote Level: 2-5 years of professional software development experience Base Salary Range: $115K ... Partner with data scientists, agronomists, and product stakeholders to translate scientific and ...

New

HealthCorps

Monitoring & Evaluation Intern

Houston, TX · Remote

Current undergraduate or graduate student studying public health, data science, social sciences, or ... Ability to work independently in a remote environment and stay on top of assigned tasks. Preferred ...

McKesson

AI Product Engineer

The Woodlands, TX · Remote

This position is remote and candidates must reside within commuting distance of either The ... Education Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or ...

McKesson

AI Product Engineer

The Woodlands, TX · Remote

This position is remote and candidates must reside within commuting distance of either The ... Education Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or ...

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All JobsAmazon Data Science JobsRemote Amazon Data Science JobsRemote Amazon Data Science Jobs in Conroe, TX

Remote Amazon Data Science information

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

AspectRemote Amazon Data ScienceRemote Amazon Data Analyst
Required CredentialsBachelor's/Master's in Data Science, Statistics, or related fields; programming skills in Python/R; experience with machine learningBachelor's in Data Analysis, Business, or related fields; proficiency in Excel, SQL, and data visualization tools
Work EnvironmentCollaborative teams, cross-functional projects, remote data science platformsRemote reporting, data interpretation, and visualization tasks within Amazon teams
Employer & Industry UsageAmazon's data science teams focusing on predictive modeling, ML, and AIAmazon's data analysis teams focusing on reporting, dashboards, and business insights

Remote Amazon Data Science involves developing machine learning models and advanced analytics, requiring programming and statistical skills. In contrast, Remote Amazon Data Analyst roles focus on interpreting data, creating reports, and supporting decision-making with less emphasis on coding. Both roles are integral to Amazon's data-driven strategies but differ in technical complexity and daily tasks.

What are popular job titles related to Remote Amazon Data Science jobs in Conroe, TX? For Remote Amazon Data Science jobs in Conroe, TX, the most frequently searched job titles are:
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Remote Amazon Data Science jobs near you

Data Scientist - Remote

NAVA Software Solutions

Houston, TX • On-site, Remote

Full-time

Re-posted 7 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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