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Machine Learning Data Associate Jobs in Texas (NOW HIRING)

As a Data Scientist, you will apply strong expertise through the use of machine learning, data mining, and information retrieval to design, prototype, and build next generation advanced analytics ...

Experience building data processing pipelines and large scale machine learning systems with ... experience in big data technologies like Spark, SQL, Snowflake/Hadoop, etc. Skilled in ...

Big Data Developer

Austin, TX · On-site

$52.50 - $68.25/hr

Experience with machine learning algorithms and automated machine learning to automate and build continuous learning data processing streams and pipelines. Data warehousing tools and techniques, such ...

Experience building data processing pipelines and large scale machine learning systems with ... experience in big data technologies like Spark, SQL, Snowflake/Hadoop, etc. Skilled in ...

Machine Learning Engineer

Austin, TX · On-site

$132K - $244K/yr

Experience building data processing pipelines and large scale machine learning systems with ... experience in big data technologies like Spark, SQL, Snowflake/Hadoop, etc. Skilled in ...

Sr. Machine Learning Engineer Duration: 12 -24 Months Location: Merrimack, NH/ Smithfield, RI ... Engineering, Computer Science, etc.) * 8+ years of proven experience in implementing Big data ...

Our employees bring deep expertise in Machine Learning, Data Science, and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from ...

Machine Learning Engineer

Austin, TX · On-site

$199K - $331K/yr

Additionally, real-world data, such as video feeds, can be encoded into neural data to project ... About the Role: Engineers on the BCI team utilize signal processing and machine learning to ...

Collaborate with senior engineers and data scientists on model deployment. * Conduct experiments and run machine learning tests. * Stay updated with the latest advancements in machine learning.

Machine learning + Spark/Hive/SQL + Python, Scala, SQL PySpark, Kafka, use of scheduling tools ... Develop and implement data pipelines and Client Pipelines to facilitate model inference (both Real ...

... data-driven decisions • Develop scalable machine learning pipelines and systems • Maintain up-to-date knowledge of emerging AI and machine learning trends • Ensure the quality and performance ...

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Machine Learning Data Associate information

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How much do machine learning data associate jobs pay per hour?

As of Jun 19, 2026, the average hourly pay for machine learning data associate in Texas is $17.46, according to ZipRecruiter salary data. Most workers in this role earn between $14.33 and $18.61 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Machine Learning Data Associate, and why are they important?

To thrive as a Machine Learning Data Associate, you need strong analytical skills, attention to detail, and a basic understanding of data annotation and labeling processes, often supported by a degree in computer science or a related field. Familiarity with data management tools, annotation platforms, and sometimes scripting languages like Python is typically required. Strong communication, collaboration, and problem-solving abilities help you work efficiently with data science teams and ensure high-quality outcomes. These skills and qualities are crucial for producing accurate datasets that directly impact the effectiveness of machine learning models.

What is the salary of ML data associate?

The salary of a Machine Learning Data Associate typically ranges from $40,000 to $70,000 annually, depending on experience, location, and company size. Entry-level positions may start lower, while experienced professionals with specialized skills in data annotation and tools like Python or SQL can earn higher salaries.

What are Machine Learning Data Associates?

Machine Learning Data Associates are professionals who support the development of machine learning models by preparing, labeling, and validating data sets. Their work ensures that data used for training algorithms is accurate, consistent, and properly annotated. They may also assist with data cleaning, quality checks, and sometimes basic data analysis tasks. This role is crucial in industries where high-quality labeled data is essential for building effective AI systems.

What is the difference between Machine Learning Data Associate vs Data Analyst?

AspectMachine Learning Data AssociateData Analyst
Required SkillsData cleaning, labeling, basic programming, understanding of ML workflowsData interpretation, visualization, statistical analysis
Work EnvironmentTech companies, AI startups, research labsBusiness, finance, marketing, healthcare sectors
Common CertificationsData Science certifications, Python, SQLExcel, Tableau, SQL certifications

The main difference is that Machine Learning Data Associates focus on preparing and labeling data specifically for machine learning models, while Data Analysts interpret data to generate insights for business decisions. Both roles require strong data skills and often overlap, but their primary objectives and work environments differ.

Is ML data associate a good job?

A Machine Learning Data Associate role involves preparing and managing data for machine learning models, often requiring skills in data cleaning, annotation, and familiarity with tools like Python or SQL. It can be a good entry-level position for those interested in AI and data science, offering opportunities to develop technical skills and gain industry experience. Job satisfaction depends on individual interests and career goals in technology and data fields.

How much do ML data associates make in the US?

Machine Learning Data Associates in the US typically earn between $35,000 and $60,000 annually, depending on experience, location, and employer. Entry-level positions may start lower, while those with specialized skills in data annotation, labeling, or familiarity with tools like Labelbox or CVAT can command higher salaries.

How does a Machine Learning Data Associate typically collaborate with data scientists and engineers within a project team?

As a Machine Learning Data Associate, you play a vital role in supporting data scientists and engineers by annotating, cleaning, and organizing large datasets to ensure high data quality. You'll frequently communicate with team members to clarify labeling guidelines, provide feedback on data inconsistencies, and report any edge cases encountered during annotation. This collaboration ensures that the datasets used for training machine learning models are accurate and comprehensive, directly impacting the success of the project. Expect regular team meetings and ongoing feedback loops to maintain alignment with evolving project requirements.

What does a machine learning data associate do?

A machine learning data associate is responsible for collecting, cleaning, and organizing data used to train machine learning models. They ensure data quality and consistency, often using tools like SQL, Python, or data annotation platforms, to support accurate model development and deployment.
Infographic showing various Machine Learning Data Associate job openings in Texas as of June 2026, with employment types broken down into 1% Internship, 1% As Needed, 69% Full Time, 24% Part Time, 1% Temporary, and 4% Contract. Highlights an 97% Physical, 1% Hybrid, and 2% Remote job distribution, with an average salary of $36,310 per year, or $17.5 per hour.

Data Scientist

Sky Consulting Inc

Houston, TX • On-site

Full-time

Posted 14 days ago


Job description

As a Data Scientist, you will apply strong expertise through the use of machine learning, data mining, and information retrieval to design, prototype, and build next generation advanced analytics engines and services. You will collaborate with cross-functional teams and business partners to define the technical problem statement and hypotheses to test. You will develop efficient and accurate analytical models which mimic business decisions and incorporate those models into analytical data products and tools. You will have the opportunity to drive current and future strategy by leveraging your analytical skills as you ensure business value and communicate the results.

Key Responsibilities

  • Collaborate with business partners to develop innovative solutions to meet objectives utilizing cutting edge techniques and tools.
  • Develop, test, and deploy data science solutions using Python, SQL, and PySpark on enterprise platforms such as Databricks.
  • Collaborate with data scientists to translate models into production-ready code.
  • Implement CI/CD pipelines and manage code repositories using GitHub Enterprise.
  • Design and optimize mathematical programming and machine learning models for real-world applications like Incentive elasticity model.
  • Expereince implementing scenario simulation algorithms.
  • Work independently to break down complex problems into actionable development tasks.
  • Ensure code quality, scalability, and maintainability in a production environment.
  • Contribute to sprint planning, documentation, and cross-functional collaboration.
  • Collaborate, coach, and learn with a growing team of experienced Data Scientists.
  • Stay connected with external sources of ideas through conferences and community engagements
Requirements
  • 8 years of experience working as a Data Scientist
  • Hands-on experience with enterprise data science solutions, preferably in retail, inventory management, or operations research.
  • Proficiency in Python, SQL, and PySpark.
  • Experience with Databricks or similar enterprise cloud environments.
  • Experience with production-level coding and deployment practices.
  • Familiarity with basic machine learning techniques and mathematical optimization methods.
  • Proficient in data science libraries and ML pipelines such as; NumPy, SciPy, scikit-learn, MLlib, PyTorch, TensorFlow.
  • Should have experience working on Price Elasticity.
  • Self-starter with an ownership mindset and the ability to work with minimal supervision.