Data Science Intern
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Job Description
About Company
WayUp is a leading platform dedicated to connecting early-career candidates with top internship and entry-level job opportunities. They partner with a wide range of organizations, from startups to Fortune 500 companies and mission-driven entities, to assist them in discovering and hiring top talent.
Job Description (Data Science Intern)
This is not a live job listing but rather an invitation to submit your interest to WayUp’s talent pool. WayUp collaborates with top companies that frequently seek Data Science Interns. By expressing your interest, WayUp will inform you when relevant opportunities become available. If your qualifications match a client’s requirements, you might be directly contacted for the next steps in their hiring process.
As a Data Science Intern, you will gain practical, hands-on experience by converting raw data into actionable insights. You will collaborate on real-world projects alongside seasoned data scientists and analysts, utilizing data to guide business decisions, enhance products, and drive strategic initiatives. This internship provides comprehensive exposure to the entire data lifecycle, encompassing data wrangling, modeling, and effective communication of insights to stakeholders.
Potential Responsibilities and Projects Include:
• Cleaning, transforming, and analyzing large datasets using programming languages such as Python, SQL, or R.
• Building predictive models and performing various statistical analyses.
• Creating data visualizations and communicating findings effectively to cross-functional teams.
• Supporting A/B testing, experiment design, or Key Performance Indicator (KPI) analysis.
• Collaborating with engineering, product, or business teams to solve real-world problems through data.
Who This Is For:
• Students anticipating graduation in late 2025, 2026, or 2027.
• Majoring in Data Science, Statistics, Computer Science, Mathematics, or a related quantitative field.
• Demonstrated experience with one or more programming languages commonly used in data science (e.g., Python, R, SQL).
• Familiarity with data manipulation libraries (e.g., pandas, dplyr) and fundamental statistical concepts.
• Possesses strong problem-solving and communication abilities.
• Exhibits curiosity and the capacity to work both independently and within small, collaborative teams.
• Bonus qualifications: Exposure to machine learning libraries (e.g., scikit-learn, XGBoost) or data visualization tools (e.g., Tableau, Power BI, matplotlib).
Technologies You May Be Exposed To:
• Programming & Analysis: Python, R, SQL, Jupyter Notebooks.
• Data Tools & Libraries: pandas, NumPy, scikit-learn, TensorFlow, Spark.
• Data Visualization: Tableau, Power BI, matplotlib, seaborn.
• Cloud Platforms: AWS (e.g., S3, Redshift), Google Cloud (e.g., BigQuery), Azure.
• Data Engineering & Pipelines: Airflow, dbt, Snowflake, Kafka.
