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Data Preparation Jobs in Texas (NOW HIRING)

Perform feature engineering, data preparation, and advanced sampling techniques for large-scale datasets. * Develop geospatial analytics and LiDAR processing solutions using industry-standard tools ...

Perform feature engineering, data preparation, and advanced sampling techniques for large-scale datasets. * Develop geospatial analytics and LiDAR processing solutions using industry-standard tools ...

Perform feature engineering, data preparation, and advanced sampling techniques for large-scale datasets. * Develop geospatial analytics and LiDAR processing solutions using industry-standard tools ...

Perform feature engineering, data preparation, and advanced sampling techniques for large-scale datasets. * Develop geospatial analytics and LiDAR processing solutions using industry-standard tools ...

Perform feature engineering, data preparation, and advanced sampling techniques for large-scale datasets. * Develop geospatial analytics and LiDAR processing solutions using industry-standard tools ...

Perform feature engineering, data preparation, and advanced sampling techniques for large-scale datasets. * Develop geospatial analytics and LiDAR processing solutions using industry-standard tools ...

Perform feature engineering, data preparation, and advanced sampling techniques for large-scale datasets. * Develop geospatial analytics and LiDAR processing solutions using industry-standard tools ...

Perform feature engineering, data preparation, and advanced sampling techniques for large-scale datasets. * Develop geospatial analytics and LiDAR processing solutions using industry-standard tools ...

TX · On-site

Perform feature engineering, data preparation, and advanced sampling techniques for large-scale datasets. * Develop geospatial analytics and LiDAR processing solutions using industry-standard tools ...

Perform feature engineering, data preparation, and advanced sampling techniques for large-scale datasets. * Develop geospatial analytics and LiDAR processing solutions using industry-standard tools ...

Sr Data Scientist

Fort Worth, TX · On-site

$90 - $100/hr

Strong experience with data preparation, cleansing, transformation, and feature engineering. * Hands-on experience designing and developing data pipelines. * Strong programming skills in Python and ...

HR Data Analyst

Coppell, TX · On-site

$25 - $30/hr

This role will provide hands-on support with recurring reporting, workforce data requests, data preparation, and foundational dashboard development. The ideal candidate is early in their analytics ...

Showing results 21-40

Data Preparation information

What is a data preparation?

A Data Preparation job involves collecting, cleaning, structuring, and transforming raw data into a usable format for analysis or machine learning. Professionals in this role ensure data quality by handling missing values, removing duplicates, and standardizing formats. They work with databases, ETL tools, and programming languages like SQL or Python. Data preparation is crucial for accurate analytics, reporting, and AI model performance.

What does a data preparation do?

A Data Preparation professional typically spends their days gathering, cleaning, and organizing raw data from various sources to make it ready for analysis. This involves identifying data discrepancies, standardizing formats, and collaborating with data analysts or engineers to resolve data quality issues. You may also automate parts of the data pipeline, document processes, and troubleshoot issues to ensure data accuracy and reliability. Working closely with cross-functional teams ensures that the data provided meets the needs of different business or research objectives. Mastery of these daily responsibilities enables smoother downstream analysis and a critical foundation for data-driven decision-making.

What are the key skills and qualifications needed to thrive in data preparation, and why are they important?

To thrive in Data Preparation, you should possess strong analytical skills, attention to detail, and proficiency in managing large datasets, often supported by a background in statistics, mathematics, or computer science. Familiarity with data wrangling tools such as SQL, Python (pandas), or specialized ETL software is typically required, and certifications in data analytics or data engineering are advantageous. Excellent problem-solving abilities, communication skills, and adaptability stand out in this role. These skills ensure data quality, streamline data workflows, and support effective collaboration with analysts and data scientists to drive organizational insights.

Infographic showing various Data Preparation job openings in Texas as of August 2026, with employment types broken down into 77% Full Time, 14% Part Time, and 9% Contract. Highlights an 100% In-person job distribution.

Full-time

This job post has expired today. Applications are no longer accepted.


Job description

Required Skills
  • 10+ years of hands-on experience in Applied Data Science, Analytics Engineering, and Systems Modeling.
  • 5+ years of client-facing, consulting, or business development experience delivering analytics solutions.
  • Expertise in statistical modeling, machine learning, and predictive analytics.
  • Strong proficiency with Python, scikit-learn, statsmodels, PyTorch, and TensorFlow.
  • Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques.
  • Strong expertise in geospatial analytics and LiDAR data processing.
  • Hands-on experience with ArcGIS, PostGIS, GeoPandas, GDAL/OGR, PDAL, and related geospatial libraries.
  • Experience working with vector, raster, point-cloud, and sensor datasets.
  • Excellent analytical, communication, and stakeholder management skills.
Roles & Responsibilities
  • Design and develop advanced machine learning and statistical models to solve complex business problems.
  • Build predictive models, time-series forecasting solutions, and causal inference frameworks.
  • Perform feature engineering, data preparation, and advanced sampling techniques for large-scale datasets.
  • Develop geospatial analytics and LiDAR processing solutions using industry-standard tools and libraries.
  • Analyze vector, raster, point-cloud, and sensor data to generate actionable insights.
  • Partner with business stakeholders to scope, design, and deliver data science solutions.
  • Present analytical findings and recommendations to technical and business audiences.
  • Optimize model performance, scalability, and deployment in production environments.
  • Mentor data scientists and promote best practices in analytics and machine learning.
  • Support innovation initiatives through advanced analytics and AI-driven solutions.