Data Science Intern – Python and Machine Learning

DataLogic

Full-time Remote United States

Job Description

Job Summary

DataLogic is seeking a highly motivated Data Science Intern with a passion for Python programming, machine learning, and data-driven problem solving. As an intern, you will gain hands-on experience working with large datasets, building predictive models, and contributing to real-world projects under the guidance of senior data scientists. This internship offers a unique opportunity to bridge academic learning with practical industry applications, preparing you for a career in data science and analytics.


Key Responsibilities

  • Assist in collecting, cleaning, and preprocessing large datasets from diverse sources.
  • Develop and implement machine learning models to solve business problems.
  • Conduct exploratory data analysis (EDA) to identify trends, patterns, and insights.
  • Collaborate with cross-functional teams to understand requirements and deliver actionable solutions.
  • Create data visualizations and reports to communicate findings effectively.
  • Stay updated with the latest trends and techniques in data science and machine learning.
  • Support documentation and presentation of project outcomes to stakeholders.

Required Skills and Qualifications

  • Strong proficiency in Python, including libraries such as pandas, NumPy, scikit-learn, and matplotlib/seaborn.
  • Understanding of machine learning algorithms such as regression, classification, clustering, and recommendation systems.
  • Knowledge of data preprocessing, feature engineering, and model evaluation techniques.
  • Familiarity with SQL and basic database management.
  • Ability to analyze complex datasets and draw meaningful insights.
  • Strong problem-solving skills and attention to detail.
  • Excellent communication and teamwork abilities.

Experience

  • Open to undergraduate or graduate students pursuing a degree in Computer Science, Data Science, Statistics, Mathematics, or related fields.
  • Prior experience with machine learning projects, either academic or personal, is highly desirable but not mandatory.
  • Familiarity with cloud platforms (AWS, GCP, Azure) or big data tools (Spark, Hadoop) is a plus.

Working Hours

  • Flexible working hours; part-time or full-time availability depending on internship schedule.
  • Remote work may be available depending on candidate location and project requirements.

Knowledge, Skills, and Abilities

  • Analytical mindset with the ability to work independently and collaboratively.
  • Strong technical writing and presentation skills to convey complex concepts clearly.
  • Ability to adapt to new tools, technologies, and workflows quickly.
  • Curiosity and eagerness to learn about business applications of data science.

Benefits

  • Hands-on experience in real-world data science projects.
  • Mentorship and guidance from experienced data scientists.
  • Opportunity to work with cutting-edge tools and technologies.
  • Flexible schedule and potential for remote work.
  • Internship certificate and strong recommendation upon successful completion.

Why Join DataLogic?

At DataLogic, we believe in nurturing talent and fostering innovation. Our team values creativity, collaboration, and continuous learning. As a Data Science Intern, you will work in a supportive environment where your ideas are valued, your skills are honed, and your career path in data science is accelerated.


How to Apply

Interested candidates are invited to submit:

  • An updated resume/CV
  • A brief cover letter explaining your interest in DataLogic and data science
  • Links to any relevant projects, GitHub repositories, or portfolios (optional)

Applications can be sent to us with the subject line: “Data Science Intern Application – [Your Name]”.

Job Details

Salary $10 – $20 / hour
Job Type Full-time
Work Mode Remote
Location Houston, Texas
Apply Before May 20, 2026
Important: We never charge any fee at any stage of the hiring process. If anyone asks for money, report it to support@freelanceshop.org.