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Remote Amazon Data Science Jobs in Friendswood, TX

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 ...

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 ...

Corebridgefinancial

Data Engineer - Snowflake

Houston, TX · On-site +1

$107K - $126K/yr

... Scientists to build data foundations for machine learning models, including feature engineering and preparing datasets for training/inference for AWS based AI/ML Platform leveraging Amazon Bedrock ...

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

Modeling Scientist

Houston, TX · On-site +1

$100K - $160K/yr

Remote Base Salary Range : $100k - $160k base salary The Modeling Scientist is responsible for ... Partner with data engineers to implement reproducible, scalable modeling pipelines * Contribute to ...

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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 cities near Friendswood, TX are hiring for Remote Amazon Data Science jobs? Cities near Friendswood, TX with the most Remote Amazon Data Science job openings:
Remote Amazon Data Science jobs near you

Data Engineer

Arva Intelligence

Houston, TX • On-site, Remote

$95K - $130K/yr

Full-time

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Job Title:                          Data Engineer 

Department:                     Modeling & Analytics

Reports to:                       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 that supports Arva’s ecosystem modeling and measurement, reporting, and verification platforms. This role sits within a multidisciplinary Data Science team and focuses on designing reliable, auditable, and scalable data systems that enable biogeochemical modeling and optimization at production scale.

In this role, the Data Engineer will design and maintain production-grade data pipelines that integrate diverse datasets including field measurements, management practices, soils, and weather with process-based ecosystem models. The role plays a critical part in ensuring data quality, reproducibility, and traceability so that scientific outputs can be translated into trusted, credit-grade results with real-world impact.

Primary Job Responsibilities

Data Pipeline and Workflow Development

  • Design, implement, and maintain scalable data pipelines supporting ecosystem and biogeochemical modeling
  • Build reproducible workflows that generate standardized model inputs and manage outputs across space, time, and scenario analysis
  • Integrate heterogeneous datasets, including field data, management data, soil data, and weather data, into modeling pipelines

Cloud Infrastructure and Data Systems

  • Develop and maintain cloud-based infrastructure to support modeling pipelines and optimization workflows
  • Implement data storage solutions using relational, spatial, and object-based databases
  • Support efficient data access and processing using platforms such as PostgreSQL, PostGIS, and cloud object storage

Data Quality, Governance, and Auditability

  • Ensure data quality, versioning, traceability, and auditability to support measurement, reporting, and verification requirements
  • Implement validation and monitoring processes to ensure reliability of model inputs and outputs
  • Support transparent, repeatable workflows suitable for regulatory and credit market review

Software Engineering and Collaboration

  • Write clean, modular, and well-documented production code that supports maintainable and scalable data systems
  • Apply software engineering best practices including testing, version control, and documentation
  • Collaborate closely with Data Science and Technology teams to align data infrastructure with modeling, analytics, and production needs

Key Competencies / Requirements

  • 3+ years demonstrated experience building and maintaining data pipelines for large, complex, and heterogeneous datasets
  • Strong proficiency in Python and modern data engineering tools, with experience writing production-grade, testable code
  • Experience working with cloud platforms, with AWS strongly preferred
  • Familiarity with containerization tools such as Docker and version control systems such as GitHub
  • Experience with relational and spatial databases, including PostgreSQL and PostGIS
  • Experience working with geospatial data formats and spatial data processing
  • Experience supporting scientific or ecosystem modeling workflows preferred
  • Familiarity with workflow orchestration tools such as Airflow or Prefect preferred
  • Bachelor’s or Master’s degree or equivalent experience in Data Engineering, Computer Science, Environmental Informatics, or a related field

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