ZipRecruiter

Log In

1

Data Preprocessing Jobs (NOW HIRING)

K-Tek Resourcing LLC

ML Engineer

Austin, TX · On-site

Perform data preprocessing, feature engineering, and model evaluation. * Utilize Scikit-learn for model development and experimentation. * Develop and maintain applications and services using Java ...

Mindlance

Data Scientist

Holtsville, NY · On-site

Strong knowledge of data preprocessing, feature engineering, and model evaluation techniques. * Familiarity with data visualization tools such as Power BI or similar. * Strong interpersonal and ...

Core One

Data Scientist

Tampa, FL

Lead and support data science efforts across the full ML lifecycle, including data collection, preprocessing, feature engineering, model development, validation, deployment, and monitoring. * Support ...

Core One

Data Scientist

Tampa, FL · On-site

Lead and support data science efforts across the full ML lifecycle, including data collection, preprocessing, feature engineering, model development, validation, deployment, and monitoring. * Support ...

Luck Stone

AI Engineer

Manakin Sabot, VA

$106K - $128K/yr

Implement and maintain machine learning pipelines, from data preprocessing to model deployment. * Troubleshoot and resolve issues related to AI models, ensuring they meet the desired accuracy and ...

System One

Research Data Scientist

Pittsburgh, PA · On-site

Establish and maintain reproducible workflows, including data preprocessing, quality control, and version control * Develop data organization standards and reporting frameworks * Generate clear data ...

C the Signs

Senior Machine Learning Engineer

Boston, MA · Remote

$125K - $165K/yr

Data Preprocessing: Clean, transform, and prepare large, complex healthcare datasets for machine learning model development. This includes handling missing values, outlier detection, feature ...

System One

Research Data Scientist

Pittsburgh, PA · On-site

Establish and maintain reproducible workflows, including data preprocessing, quality control, and version control * Develop data organization standards and reporting frameworks * Generate clear data ...

next page

Showing results 1-20

All JobsData Preprocessing Jobs

Data Preprocessing information

See salary details

$46K

$165K

$243.5K

How much do data preprocessing jobs pay per year?

As of Jun 29, 2026, the average yearly pay for data preprocessing in the United States is $165,018.00, according to ZipRecruiter salary data. Most workers in this role earn between $133,500.00 and $170,000.00 per year, depending on experience, location, and employer.

What is the highest paying job in data?

In data-related fields, roles such as Data Science Director, Machine Learning Engineer, and Chief Data Officer tend to have the highest salaries, often exceeding six figures annually. These positions typically require advanced skills in data analysis, programming, and leadership, along with extensive experience and relevant certifications.

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.

Is 40 too late for data science?

Data preprocessing is a key step in data science, and individuals can enter the field at any age. Many data scientists start later in life, and acquiring skills in programming, statistics, and tools like Python or R can facilitate entry regardless of age.

What do you do in data preprocessing?

Data preprocessing involves cleaning and transforming raw data to prepare it for analysis or modeling. This includes tasks such as handling missing values, removing duplicates, normalizing data, and encoding categorical variables, often using tools like Python or R. It is a crucial step to ensure data quality and improve model performance.

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.

Will AI replace data analysts?

AI is transforming data analysis by automating routine tasks such as data cleaning and basic reporting, but data analysts are still essential for interpreting complex insights, making strategic decisions, and applying domain knowledge. The role is evolving to include skills in machine learning tools and programming languages like Python or R, but human expertise remains critical for nuanced analysis and contextual understanding.

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.
More about Data Preprocessing jobs
What cities are hiring for Data Preprocessing jobs? Cities with the most Data Preprocessing job openings:
What states have the most Data Preprocessing jobs? States with the most job openings for Data Preprocessing jobs include:
What job categories do people searching Data Preprocessing jobs look for? The top searched job categories for Data Preprocessing jobs are:
Data Preprocessing jobs near you
Infographic showing various Data Preprocessing job openings in the United States as of June 2026, with employment types broken down into 50% Internship, and 50% Full Time. Highlights an 100% In-person job distribution, with an average salary of $165,018 per year, or $79.3 per hour.
ML Engineer

ML Engineer

K-Tek Resourcing LLC

Austin, TX • On-site

Other

Posted 6 days ago

Key responsibilities

  • Design, develop, and implement machine learning models for business use cases.

  • Build and maintain scalable ML pipelines for training, testing, and deployment.

  • Integrate machine learning models into production environments.

Job description

Job Summary

We are seeking a skilled Machine Learning Engineer to design, develop, and deploy machine learning solutions that address complex business problems. The ideal candidate will have strong experience in Python, Java, Scikit-learn, and machine learning algorithms, with the ability to build scalable and production-ready ML applications.

The candidate will work closely with data scientists, software engineers, and business stakeholders to develop predictive models, automate analytical processes, and integrate machine learning solutions into enterprise applications.

Key Responsibilities
  • Design, develop, and implement machine learning models for business use cases.
  • Build and maintain scalable ML pipelines for training, testing, and deployment.
  • Develop predictive and classification models using machine learning algorithms.
  • Perform data preprocessing, feature engineering, and model evaluation.
  • Utilize Scikit-learn for model development and experimentation.
  • Develop and maintain applications and services using Java and Python.
  • Integrate machine learning models into production environments.
  • Collaborate with cross-functional teams to understand business requirements.
  • Monitor model performance and optimize models for accuracy and scalability.
  • Document technical solutions, methodologies, and model performance metrics.
Required Skills
  • Strong experience in Python programming.
  • Hands-on experience with Scikit-learn (Scikit library).
  • Strong programming skills in Java.
  • Solid understanding of Machine Learning algorithms and techniques.
  • Experience with supervised and unsupervised learning models.
  • Experience with data preprocessing and feature engineering.
  • Strong knowledge of statistics and model evaluation techniques.
  • Experience working with large datasets.

Apply