Data Scientist (DMS Project)
Houston Full-time Mid-level Data Science
At a glance
Mid-level Data Science role at Foxconn Industrial Internet - FII. Houston · full-time · no salary stated · we estimate $115,300–$175,500 across the United States.
Estimated pay
Foxconn Industrial Internet - FII has not published a salary for this role. The figure below is our estimate, not theirs.
$115,300 – $175,500
What the middle half of mid-level data science roles in the United States paid, among postings that did state a salary.
Worked out from 425 postings that stated pay, median $145,000. Last updated Oct 24, 2026.
RoleData Science
SeniorityMid-level
LocationHouston
EmploymentFull-time
Role brief
dataroles summary, based on the employer's posting.
What you'll do
- Turn operational and business user needs into system designs, data workflows, and reporting specifications
- Build and maintain SQL and Python ETL pipelines that make raw data usable
- Develop Python APIs and backend services for data access, integrations, and applications
- Create and improve Tableau dashboards, prioritizing accurate data and practical use
- Track data quality and system reliability, resolve issues, and tune performance as usage grows
What you bring
- You have hands-on experience cleaning and transforming data into structured datasets
- You use Python, SQL, and Git in real projects, with strong SQL skills
- You have backend development experience and can work with API design for integrations and data access
- You can turn user needs into deployable systems or dashboards and explain technical choices to cross-functional teams
Who this fits
This role suits someone focused on building and operating dependable data systems, rather than research or model development. You’ll work with global engineering and data partners, including teams in Asia, so comfort collaborating across time zones is useful.
From the employer
Job Summary
This role is a software- and data engineering–focused position responsible for turning real-world user needs into production-ready data pipelines, backend services, and data-driven applications.
The primary focus is on data processing, system integration, performance, and reliability, working closely with business and operational users to continuously improve systems and dashboards so that data can be accurately and effectively used in real operational scenarios.
This role emphasizes building and operating reliable data systems rather than research-oriented modeling or algorithm development.
Key Responsibilities
●Work closely with internal users (e.g., operations, manufacturing, or business teams) to understand real usage scenarios and translate requirements into system designs, data pipelines, and reporting specifications
●Design, build, and maintain reliable data pipelines (ETL) using SQL and Python
●Develop and maintain backend services and APIs to support data access, system integration, and application functionality
●Build and enhance BI dashboards and visualizations (e.g., Tableau) with a strong focus on data accuracy, usability, and real-world adoption
●Collaborate with engineers, IT teams, and cross-functional partners on system integration and workflow optimization
●Optimize system performance and architecture as data volume and usage patterns evolve
●Proactively monitor data quality and system reliability, identifying and resolving issues that impact user experience
●Collaborate with global engineering and data teams, including partners in Asia, to align on system design, data workflows, and implementation details.
This role values production-ready systems that are stable, accurate, and actually used, rather than model-centric or research-driven solutions.
Qualifications
Must Have
●Hands-on experience with data processing, cleaning, and transformation, turning raw data into structured and usable datasets
●Proficiency in Python, SQL, and Git, with experience applying them in real projects
●Strong SQL skills; experience participating in or being familiar with database schema design, with an understanding of how data models support usage scenarios
●Backend development experience; experience participating in or being familiar with building APIs in Python to support system integration and data access
●Experience working with cross-functional teams and the ability to clearly explain technical solutions and trade-offs
●Experience collaborating with users to translate real-world requirements into deployable systems, data workflows, or dashboards
Nice to Have
●Experience with Linux and Docker, including system deployment or operations
●Familiarity with CI/CD pipelines
●Experience with database performance tuning or large-scale data processing
●Ability to communicate in Spanish or Chinese for work-related discussions
●Comfortable working in a global, cross-time-zone collaboration environment
This role is a software- and data engineering–focused position responsible for turning real-world user needs into production-ready data pipelines, backend services, and data-driven applications.
The primary focus is on data processing, system integration, performance, and reliability, working closely with business and operational users to continuously improve systems and dashboards so that data can be accurately and effectively used in real operational scenarios.
This role emphasizes building and operating reliable data systems rather than research-oriented modeling or algorithm development.
Key Responsibilities
●Work closely with internal users (e.g., operations, manufacturing, or business teams) to understand real usage scenarios and translate requirements into system designs, data pipelines, and reporting specifications
●Design, build, and maintain reliable data pipelines (ETL) using SQL and Python
●Develop and maintain backend services and APIs to support data access, system integration, and application functionality
●Build and enhance BI dashboards and visualizations (e.g., Tableau) with a strong focus on data accuracy, usability, and real-world adoption
●Collaborate with engineers, IT teams, and cross-functional partners on system integration and workflow optimization
●Optimize system performance and architecture as data volume and usage patterns evolve
●Proactively monitor data quality and system reliability, identifying and resolving issues that impact user experience
●Collaborate with global engineering and data teams, including partners in Asia, to align on system design, data workflows, and implementation details.
This role values production-ready systems that are stable, accurate, and actually used, rather than model-centric or research-driven solutions.
Qualifications
Must Have
●Hands-on experience with data processing, cleaning, and transformation, turning raw data into structured and usable datasets
●Proficiency in Python, SQL, and Git, with experience applying them in real projects
●Strong SQL skills; experience participating in or being familiar with database schema design, with an understanding of how data models support usage scenarios
●Backend development experience; experience participating in or being familiar with building APIs in Python to support system integration and data access
●Experience working with cross-functional teams and the ability to clearly explain technical solutions and trade-offs
●Experience collaborating with users to translate real-world requirements into deployable systems, data workflows, or dashboards
Nice to Have
●Experience with Linux and Docker, including system deployment or operations
●Familiarity with CI/CD pipelines
●Experience with database performance tuning or large-scale data processing
●Ability to communicate in Spanish or Chinese for work-related discussions
●Comfortable working in a global, cross-time-zone collaboration environment
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