Talantix.io
LIME Medical•04.03.26
AI SCORE 8.2

Data Science Engineer Intern - Remote

$40K–$60K/year
RemoteAI/Big Data
Python•SQL•Machine Learning•Data Analysis•ETL•Data Engineering

About the Role

We are looking for a passionate Data Science Engineer Intern to join our dynamic team in a fully remote capacity. As a Data Science Engineer Intern, you'll have the opportunity to work on exciting projects that leverage machine learning and data analysis to drive impactful solutions. This role is perfect for students or early-career professionals eager to gain hands-on experience in data science and machine learning.

What You'll Do

  • Collaborate with the data science team to develop and implement machine learning models.
  • Assist in data collection, cleaning, and preprocessing to ensure high-quality datasets.
  • Work on data pipelines and ETL processes to streamline data flow and transformation.
  • Participate in the design and development of data-driven applications and solutions.
  • Engage in continuous learning and contribute to team discussions on best practices in data science.

Requirements

  • Currently pursuing a degree in Data Science, Computer Science, Mathematics, or a related field.
  • Familiarity with programming languages such as Python and SQL.
  • Basic understanding of machine learning concepts and data analysis techniques.
  • Experience with data visualization tools and libraries is a plus.
  • Strong analytical and problem-solving skills.

Nice to Have

  • Knowledge of data engineering principles and practices.
  • Exposure to cloud platforms and MLOps tools.
  • Familiarity with Jira or similar project management tools.

What We Offer

  • Flexible work hours and the ability to work from anywhere.
  • Opportunity to learn from experienced professionals in the field.
  • Access to continuous learning resources and mentoring.
  • Be part of an inspiring team where your ideas and contributions matter.
  • Gain valuable experience that will enhance your career prospects in data science.
Why This Job8.2 of 10

This Data Science Engineer Intern position offers a unique opportunity to gain hands-on experience in a supportive remote environment. Ideal for students eager to learn.

Salary Range
Required
0/1
Optional
0/1
Bonus
0/1

Who Will Succeed Here

→

Proficient in Python for data manipulation and analysis, with familiarity in libraries such as Pandas and NumPy, enabling efficient data processing and exploration.

→

Strong understanding of SQL for database querying, including the ability to design and optimize queries to extract insights from large datasets, essential for ETL processes.

→

Eager and adaptable mindset, with a commitment to learning and applying machine learning algorithms using tools like Scikit-learn, suited for a remote work environment where self-motivation is key.

Learning Resources

→Python for Data Analysisbook
→Learn SQL Basics for Data Sciencecourse
→Machine Learning with Python - Full Course for Beginnersvideo

Career Path

Data Science Engineer Intern(Now)→Data Analyst or Junior Data Scientist(1-2 years)→Data Scientist or Data Engineer(3-5 years)

Market Overview

Market Size 2024
$30B
Annual Growth
22.5%
AI Adoption
45%
Investment
+150%
Labour Demand
+40%
Avg Salary
$85K

Skills & Requirements

Required
PythonSQLMachine Learning
Growing in Demand
Deep LearningCloud Computing (AWS, Azure)Big Data Technologies (Hadoop, Spark)
Declining
R ProgrammingExcel for Data Analysis

Domain Trends

Increased Automation in Data Processing
By 2025, over 60% of data processing tasks will be automated, leading to a greater demand for Data Science Engineers who can implement and manage these automated systems.
Rise of No-Code/Low-Code Platforms
No-code and low-code platforms are projected to grow by 25% annually, which is shifting the focus towards integrating Python with these platforms for data workflows.
Growing Importance of Ethical AI
Around 70% of organizations are prioritizing ethical AI practices, creating a demand for Data Science Engineers who can ensure compliance and ethical considerations in their models.

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