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Data Preprocessing Jobs in Texas (NOW HIRING)

Experience with data preprocessing, feature engineering, SQL, and data manipulation. * Familiarity with AI model evaluation, optimization, and hyperparameter tuning. * Strong analytical, problem ...

Experience with data preprocessing, feature engineering, SQL, and data manipulation. * Familiarity with AI model evaluation, optimization, and hyperparameter tuning. * Strong analytical, problem ...

Experience with data preprocessing, feature engineering, SQL, and data manipulation. * Familiarity with AI model evaluation, optimization, and hyperparameter tuning. * Strong analytical, problem ...

Experience with data preprocessing, feature engineering, SQL, and data manipulation. * Familiarity with AI model evaluation, optimization, and hyperparameter tuning. * Strong analytical, problem ...

Experience with data preprocessing, feature engineering, SQL, and data manipulation. * Familiarity with AI model evaluation, optimization, and hyperparameter tuning. * Strong analytical, problem ...

Experience with data preprocessing, feature engineering, SQL, and data manipulation. * Familiarity with AI model evaluation, optimization, and hyperparameter tuning. * Strong analytical, problem ...

Experience with data preprocessing, feature engineering, SQL, and data manipulation. * Familiarity with AI model evaluation, optimization, and hyperparameter tuning. * Strong analytical, problem ...

TX ยท On-site

Experience with data preprocessing, feature engineering, SQL, and data manipulation. * Familiarity with AI model evaluation, optimization, and hyperparameter tuning. * Strong analytical, problem ...

Experience with data preprocessing, feature engineering, SQL, and data manipulation. * Familiarity with AI model evaluation, optimization, and hyperparameter tuning. * Strong analytical, problem ...

Experience with data preprocessing, feature engineering, SQL, and data manipulation. * Familiarity with AI model evaluation, optimization, and hyperparameter tuning. * Strong analytical, problem ...

Experience with data preprocessing, feature engineering, SQL, and data manipulation. * Familiarity with AI model evaluation, optimization, and hyperparameter tuning. * Strong analytical, problem ...

Experience with data preprocessing, feature engineering, SQL, and data manipulation. * Familiarity with AI model evaluation, optimization, and hyperparameter tuning. * Strong analytical, problem ...

Experience with data preprocessing, feature engineering, SQL, and data manipulation. * Familiarity with AI model evaluation, optimization, and hyperparameter tuning. * Strong analytical, problem ...

Gathering unstructured and structured data from multiple sources, then cleaning and preprocessing it to ensure data quality and usability. Exploratory Data Analysis (EDA): Analyzing data to uncover ...

Experience with data preprocessing, feature engineering, SQL, and data manipulation. * Familiarity with AI model evaluation, optimization, and hyperparameter tuning. * Strong analytical, problem ...

Experience with data preprocessing, feature engineering, SQL, and data manipulation. * Familiarity with AI model evaluation, optimization, and hyperparameter tuning. * Strong analytical, problem ...

Experience with data preprocessing, feature engineering, SQL, and data manipulation. * Familiarity with AI model evaluation, optimization, and hyperparameter tuning. * Strong analytical, problem ...

Experience with data preprocessing, feature engineering, SQL, and data manipulation. * Familiarity with AI model evaluation, optimization, and hyperparameter tuning. * Strong analytical, problem ...

Showing results 41-60

Data Preprocessing information

What is data preprocessing?

Data preprocessing is the process of cleaning, transforming, and organizing raw data into a usable format for analysis or machine learning. It involves steps such as handling missing values, removing duplicates, normalizing or scaling data, and encoding categorical variables. Proper data preprocessing helps improve the quality and performance of predictive models by ensuring the data is accurate, consistent, and suitable for analysis.

What are the key skills and qualifications needed to thrive as a data preprocessing specialist, and why are they important?

To thrive as a Data Preprocessing Specialist, you need a strong background in statistics, data cleaning, and data transformation, often supported by a degree in computer science, data science, or a related field. Proficiency with tools such as Python (pandas, NumPy), SQL, and data visualization platforms is typically essential, along with familiarity with data management systems. Attention to detail, problem-solving abilities, and effective communication are standout soft skills in this position. These skills are crucial for ensuring high-quality, reliable datasets that underpin accurate data analysis and machine learning outcomes.

What are some common challenges faced in a data preprocessing role, and how can they be effectively managed?

Professionals in Data Preprocessing often encounter challenges such as handling incomplete or inconsistent data, managing large datasets, and ensuring data quality before analysis. Addressing these issues typically involves using specialized tools to automate data cleaning, establishing clear data validation rules, and collaborating closely with data engineers and analysts. Staying updated with best practices and leveraging scripting languages like Python or R can also streamline the preprocessing workflow, making it easier to deliver reliable and accurate datasets for downstream analysis.

What is the difference between Data Preprocessing vs Data Analysis?

AspectData PreprocessingData Analysis
Primary FocusCleaning, transforming, and preparing raw data for analysisInterpreting data to extract insights and support decision-making
Skills RequiredData cleaning, scripting, understanding of data formatsStatistical analysis, data visualization, critical thinking
Work EnvironmentData engineering teams, data science projectsBusiness intelligence, research, data science teams
Tools UsedPython, R, SQL, ETL toolsExcel, Tableau, R, Python, statistical software

While data preprocessing involves preparing raw data for analysis by cleaning and transforming it, data analysis focuses on interpreting the prepared data to uncover trends and insights. Both roles are essential in the data pipeline but serve different purposes in the data lifecycle.

What are popular job titles related to Data Preprocessing jobs in Texas?

For Data Preprocessing jobs in Texas, the most frequently searched job titles are:

What cities in Texas are hiring for Data Preprocessing jobs?

Cities in Texas with the most Data Preprocessing job openings:

Infographic showing various Data Preprocessing job openings in Texas as of June 2026, with employment types broken down into 40% Internship, and 60% Full Time. Highlights an 100% In-person job distribution.

Generative AI Engineer

PB consulting

Dalworthington Gardens, TX โ€ข On-site

Full-time

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


Job description

We are seeking a skilled Generative AI Engineer to design, develop, and deploy advanced AI solutions that solve complex business challenges. The ideal candidate will have strong expertise in Python, Large Language Models (LLMs), machine learning, deep learning, and Natural Language Processing (NLP), with hands-on experience building and optimizing generative AI applications using open-source models and modern AI frameworks.

Roles and Responsibilities
  • Design, develop, and implement advanced AI and Generative AI solutions to address complex business requirements.

  • Collaborate with engineers, researchers, product managers, and business stakeholders to translate business needs into scalable AI solutions.

  • Collect, clean, prepare, and engineer data for training, fine-tuning, and evaluating AI models while ensuring data quality and integrity.

  • Develop and optimize machine learning, deep learning, and NLP models for enterprise applications.

  • Build and deploy Generative AI solutions using Large Language Models (LLMs), text generation, and text-to-image generation techniques.

  • Evaluate, compare, and optimize AI model architectures, hyperparameters, and performance metrics.

  • Apply responsible AI practices by identifying model bias, improving fairness, and ensuring ethical AI development.

  • Develop scalable AI solutions that integrate with enterprise applications and cloud platforms.

  • Participate in model testing, deployment, monitoring, and continuous improvement.

  • Contribute to AI best practices, technical documentation, and knowledge sharing across teams.

Required Skills
  • Strong proficiency in Python with major machine learning and deep learning libraries.

  • Hands-on experience with open-source Large Language Models (LLMs) such as Llama, Dolly, or similar models.

  • Strong knowledge of Machine Learning, Deep Learning, Natural Language Processing (NLP), neural networks, transformers, supervised and unsupervised learning.

  • Experience with Generative AI techniques including text generation, text-to-image generation, and Generative Adversarial Networks (GANs).

  • Experience with data preprocessing, feature engineering, SQL, and data manipulation.

  • Familiarity with AI model evaluation, optimization, and hyperparameter tuning.

  • Strong analytical, problem-solving, and communication skills.

Preferred Skills
  • Experience with cloud platforms including AWS, Microsoft Azure, or Google Cloud Platform (GCP).

  • Experience developing AI applications in both on-premises and cloud environments.

  • Knowledge of CI/CD pipelines and AI application deployment practices.

  • Familiarity with MLOps, model lifecycle management, and scalable AI deployment architectures.

Qualifications
  • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.

  • Experience designing, developing, and deploying enterprise AI or Generative AI solutions.

  • Ability to work effectively in cross-functional teams and deliver high-quality AI solutions in an agile environment.