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Data Preprocessing Jobs in Texas (NOW HIRING)

AI/ML Engineer

Dallas, TX · On-site

$113K - $136K/yr

Work with large datasets and perform data preprocessing, feature engineering, and model evaluation. Collaborate with data engineers, software developers, and business stakeholders to understand ...

AI/ML Engineer

Dallas, TX · On-site

$113K - $136K/yr

Work with large datasets and perform data preprocessing, feature engineering, and model evaluation. Collaborate with data engineers, software developers, and business stakeholders to understand ...

AI/ML Engineer

Dallas, TX · On-site

$113K - $136K/yr

Work with large datasets and perform data preprocessing, feature engineering, and model evaluation. Collaborate with data engineers, software developers, and business stakeholders to understand ...

Senior AI Engineer

Houston, TX · On-site

$99K - $137K/yr

Expertise in AI/ML algorithms, deep learning frameworks (e.g., TensorFlow, PyTorch), and the end-to-end AI development lifecycle, including data preprocessing, model training, and deployment.

Conduct exploratory data analysis, feature engineering, and data preprocessing to solve business problems. * Own, operate, and enhance our proprietary risk-based pricing engine (a production Python ...

Conduct exploratory data analysis, feature engineering, and data preprocessing to solve business problems. * Own, operate, and enhance our proprietary risk-based pricing engine (a production Python ...

Conduct exploratory data analysis, feature engineering, and data preprocessing to solve business problems. * Own, operate, and enhance our proprietary risk-based pricing engine (a production Python ...

Conduct exploratory data analysis, feature engineering, and data preprocessing to solve business problems. * Own, operate, and enhance our proprietary risk-based pricing engine (a production Python ...

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 ...

... data analysis, including preprocessing, feature engineering, and leveraging Generative AI algorithms for novel solutions. • Lead cross-functional collaborations to integrate Generative AI models ...

Execute comprehensive data analysis, including preprocessing, feature engineering, and leveraging Generative AI algorithms for novel solutions. * Lead cross‑functional collaborations to integrate ...

... data analysis, including preprocessing, feature engineering, and leveraging Generative AI algorithms for novel solutions. · Lead cross-functional collaborations to integrate Generative AI models ...

Execute comprehensive data analysis, including preprocessing, feature engineering, and leveraging Generative AI algorithms for novel solutions. * Lead cross-functional collaborations to integrate ...

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 ...

Perform data preprocessing and feature engineering. * Train, evaluate, and improve ML models. * Work with structured and unstructured datasets. * Develop Generative AI and LLM-based applications.

Showing results 21-40

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 job categories do people searching Data Preprocessing jobs in Texas look for?

The top searched job categories for Data Preprocessing jobs in Texas 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.

AI/ML Engineer

Dallas, TX • On-site

Winaxis
IT Services • 11 - 50 employees

$113K - $136K/yr

Full-time

Re-posted 26 days ago


Job description

About the Role:- We are seeking a talented and innovative AI/ML Engineer to design, develop, and deploy machine learning and artificial intelligence solutions that solve real-world business problems. The ideal candidate should have strong expertise in machine learning algorithms, data processing, model deployment, and cloud technologies. Key Responsibilities Design, develop, train, and optimize Machine Learning and Deep Learning models. Build and maintain scalable data pipelines for model training and inference. Develop AI-powered applications using NLP, Computer Vision, Generative AI, and predictive analytics techniques. Deploy machine learning models into production environments using MLOps best practices. Work with large datasets and perform data preprocessing, feature engineering, and model evaluation. Collaborate with data engineers, software developers, and business stakeholders to understand requirements and deliver AI solutions. Monitor model performance and implement continuous improvements. Research and evaluate emerging AI technologies, frameworks, and industry trends. Develop APIs and microservices for AI model integration. Ensure data security, model governance, and compliance standards are maintained. Required Qualifications Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Mathematics, Statistics, or a related field. Strong programming skills in Python. Experience with Machine Learning libraries such as: TensorFlow PyTorch Scikit-learn XGBoost Strong understanding of: Supervised and Unsupervised Learning Deep Learning Neural Networks Natural Language Processing (NLP) Computer Vision Reinforcement Learning (preferred) Experience with SQL and NoSQL databases. Knowledge of model deployment frameworks such as Docker, Kubernetes, and MLflow. Experience working with cloud platforms such as AWS, Azure, or GCP. Familiarity with version control systems like Git. Preferred Qualifications Experience with Generative AI technologies and Large Language Models (LLMs). Hands-on experience with: LangChain LlamaIndex Hugging Face OpenAI APIs Vector Databases (Pinecone, Weaviate, ChromaDB, FAISS) Experience in RAG (Retrieval-Augmented Generation) implementations. Knowledge of MLOps tools and CI/CD pipelines. Experience with Databricks and Apache Spark. Technical Skills Python SQL TensorFlow PyTorch Scikit-learn Pandas NumPy Apache Spark MLflow Docker Kubernetes AWS/Azure/GCP Git REST APIs Generative AI & LLMs Soft Skills Strong analytical and problem-solving abilities. Excellent communication and collaboration skills. Ability to work independently and in a team environment. Strong attention to detail and commitment to quality. Nice to Have AI Agent Development Multi-Agent Systems Prompt Engineering Fine-tuning LLMs Knowledge Graphs MLOps Certification Cloud Certifications (AWS, Azure, GCP)