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Data Preprocessing Jobs in Greenbrier, TN (NOW HIRING)

Tao Digital Solutions, Inc.

Data Scientist

Nashville, TN · On-site

$60K/yr

Data Acquisition, Cleaning & Preprocessing * * Assist in collecting, validating, and preprocessing structured and unstructured datasets from internal and third-party financial systems. * Perform data ...

Varsity Tutors

Machine Learning Tutor

Nashville, TN · Remote

$18 - $40/hr

Guides students through data preprocessing, feature selection, building and comparing classification and regression models, implementing clustering algorithms, and interpreting confusion matrices and ...

Elevance Health

Gen AI Engineer

Nashville, TN · Hybrid

Responsible for the design and development of custom ML, Gen AI, NLP, LLM Models for batch and stream processing-based AI ML pipelines including data ingestion, preprocessing modules, search and ...

Elevance Health

Gen AI Engineer

Nashville, TN · Hybrid

Responsible for the design and development of custom ML, Gen AI, NLP, LLM Models for batch and stream processing-based AI ML pipelines including data ingestion, preprocessing modules, search and ...

TheIncLab

Senior Machine Learning Engineer

Nashville, TN

$100K - $138K/yr

Data validation and quality checks * Feature engineering and preprocessing * Data augmentation strategies for training robustness * Train, tune, and debug models, addressing issues such as ...

TheIncLab

Senior Machine Learning Engineer

Nashville, TN · On-site

$100K - $138K/yr

Data validation and quality checks * Feature engineering and preprocessing * Data augmentation strategies for training robustness * Train, tune, and debug models, addressing issues such as ...

All JobsData Preprocessing JobsData Preprocessing Jobs in Greenbrier, TN

Data Preprocessing information

See Greenbrier, TN salary details

$39.3K

$141.1K

$208.3K

How much do data preprocessing jobs pay per year?

As of Jun 28, 2026, the average yearly pay for data preprocessing in Greenbrier, TN is $141,130.00, according to ZipRecruiter salary data. Most workers in this role earn between $114,200.00 and $145,400.00 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.
Data Preprocessing jobs near you
Infographic showing various Data Preprocessing job openings in Greenbrier, TN as of June 2026, with employment types broken down into 42% Internship, and 58% Full Time. Highlights an 100% In-person job distribution, with an average salary of $141,130 per year, or $67.9 per hour.

Data Scientist

Tao Digital Solutions, Inc.

Nashville, TN • On-site

$60K/yr

Full-time

Posted 26 days ago

Job description

Job Title : Data Scientist
Location : Hermitage, TN
Wage : $60,000
Job Duties :
  1. Data Acquisition, Cleaning & Preprocessing

    • Assist in collecting, validating, and preprocessing structured and unstructured datasets from internal and third-party financial systems.
    • Perform data quality checks, resolve anomalies, and maintain metadata using SQL, Python (Pandas), and Excel.

  1. Exploratory Data Analysis (EDA)

    • Conduct exploratory data analysis to identify trends, outliers, and correlations within financial and operational datasets.
    • Support the preparation of data summaries, distribution checks, and hypothesis validations.

  1. Automation & Data Pipeline Support

    • Assist in developing automation scripts and data pipelines using Python, Excel macros, and RPA tools (e.g., Blue Prism) to streamline data ingestion and transformation.
    • Support version control and CI/CD practices using Git repositories.

  1. Predictive Modeling & Forecasting

    • Support senior data scientists in building and validating statistical and machine learning models to forecast revenue trends, customer churn, or financial health.
    • Participate in refining time-series models and basic regressions using Python (Scikit-learn, StatsModels).

  1. Financial & Business Analysis

    • Contribute to financial modeling by evaluating key metrics (e.g., EBITDA, revenue growth, margins) and integrating external macroeconomic indicators into models.
    • Work alongside business analysts to align technical models with stakeholder requirements.

  1. Data Visualization & Dashboarding

    • Develop and maintain interactive dashboards using Tableau, Power BI, or Python (Matplotlib, Seaborn) to communicate insights to internal stakeholders.
    • Automate reporting templates and visualization tools for monthly and quarterly updates.

  1. Documentation & Compliance

    • Maintain comprehensive documentation for model assumptions, workflows, data dictionaries, and QA protocols.
    • Ensure data practices align with internal governance policies and industry regulations (e.g., GDPR, SOX).

  1. Collaboration & Communication

    • Work closely with cross-functional teams including finance, data engineering, and business strategy teams to align analytical efforts with organizational goals.
    • Participate in sprint meetings and contribute to shared knowledge repositories.

  1. Model Monitoring & Feedback Loops

    • Assist in tracking model performance and accuracy post-deployment using standard KPIs (e.g., RMSE, MAE).
    • Help integrate user feedback and error analysis into model retraining cycles.

  1. Professional Development

    • Attend internal workshops and training sessions on data science tools and methodologies.
    • Stay informed of advancements in machine learning, financial modeling, and analytics platforms.

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