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Data Preprocessing Jobs in Maryland (NOW HIRING)

Perform data cleaning, preprocessing, and feature engineering on structured and unstructured datasets. * Develop pipelines for model training, evaluation, and deployment. * Collaborate with cross ...

Perform appropriate data preprocessing steps to prepare data for ingestion into pipelines * Conduct in-depth analysis of clinical trial and clinical trial operations data * Monitor key study metrics ...

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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 cities in Maryland are hiring for Data Preprocessing jobs?

Cities in Maryland with the most Data Preprocessing job openings:

Infographic showing various Data Preprocessing job openings in Maryland as of September 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

Data Scientist

Baltimore, MD โ€ข On-site

Contractor

Re-posted 21 days ago


Job description

Job Title: Data Scientist

Location: Baltimore, MD (Onsite)

Hiring Type: Contract

Job Summary: We are seeking a highly skilled Data Scientist with strong expertise in Python, NLP, and Generative AI. The ideal candidate will have hands-on experience in building intelligent models using Natural Language Processing (NLP) techniques, working with large datasets, and leveraging LLMs (Large Language Models) to solve real-world business problems.


Key Responsibilities:
•Design, develop, and deploy machine learning and NLP models for text-based applications.
•Work extensively with Python libraries such as Pandas, NumPy, and Scikit-learn for data processing and analysis.
•Implement and optimize NLP techniques using tools like NLTK, SpaCy, or Transformers.
•Build and integrate solutions using Large Language Models (LLMs) and Generative AI frameworks.
•Perform data cleaning, preprocessing, and feature engineering on structured and unstructured datasets.
•Develop pipelines for model training, evaluation, and deployment.
•Collaborate with cross-functional teams to translate business requirements into AI-driven solutions.
•Stay updated with the latest advancements in AI, NLP, and Gen AI technologies.
 

Required Skills & Qualifications:
•Strong programming experience in Python.
•Hands-on experience with NLP (Natural Language Processing) techniques.
•Proficiency in libraries such as NLTK, Pandas, NumPy, Scikit-learn.
•Experience working with LLMs (e.g., GPT, BERT, etc.).
•Knowledge of Generative AI concepts and frameworks.
•Experience in data preprocessing, data analysis, and model building.
•Understanding of machine learning algorithms and model evaluation techniques.
•Strong problem-solving and analytical skills.
 

Preferred Qualifications:
•Experience with Deep Learning frameworks like TensorFlow or PyTorch.
•Familiarity with Hugging Face Transformers or similar libraries.
•Experience in deploying models using APIs or cloud platforms (AWS, Azure, GCP).
•Knowledge of MLOps practices and model lifecycle management.
 

Education Details:
•Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or related field.