Data Science Intern

Job Description

Data Science Intern | Stealth Startup

The Tone:
This is an internship at Stealth Startup, located remotely within the United States time zones. The company is hiring on behalf of a client who is actively solving real-world business problems through data science. This role offers a chance to contribute directly to data-driven solutions and make a tangible impact within a collaborative team environment. Interns are expected to translate data analysis into actionable insights and build foundational data science components.

The TL;DR
• Role: Internship
• Type: Temporary (3–6 months)
• Location: Remote, United States

• Team: Reports to senior data scientists within a solutions-driven data science team, collaborating with engineering, product, and business stakeholders.
• Mission: Design and implement data-driven solutions to solve business problems and translate data analysis into impactful outcomes.
• Tech Stack: Python (Pandas, NumPy, Scikit-learn), Matplotlib, Seaborn, SQL

What You’ll Actually Do
• Solutions Design: Assist in designing and implementing data-driven solutions to address business challenges.
• Data Management: Work with structured and unstructured datasets to perform cleaning, preprocessing, and analysis.
• Model Development: Build and evaluate machine learning models under the guidance of senior data scientists.
• Insight Generation: Conduct exploratory data analysis (EDA) and present actionable insights to stakeholders.
• Infrastructure Contribution: Contribute to the development and maintenance of data pipelines, dashboards, and reporting tools.

The Must-Haves
• Background: Student or recent graduate pursuing or having completed a degree in Data Science, Computer Science, Statistics, Mathematics, or a related analytical field.
• Experience: Strong foundation in Python, including libraries like Pandas, NumPy, and Scikit-learn; an understanding of core machine learning concepts such as regression, classification, and clustering; practical experience with data visualization tools (e.g., Matplotlib, Seaborn); and familiarity with SQL for database interaction.
• Skills: Strong analytical thinking and problem-solving abilities, proficiency in data analysis and interpretation, fundamental understanding of machine learning principles, and effective communication skills for storytelling with data.
• Bonus: Exposure to deep learning frameworks (TensorFlow, PyTorch), experience with cloud platforms (AWS, GCP, Azure), knowledge of real-world data science applications (e.g., recommendation systems, NLP, forecasting), prior internship or project experience in data science or analytics, and familiarity with version control systems (Git).