Job Summary

Data Science Summer Intern

Lititz, PA Remote

Entry Level

Internship

Legal residents of one of the following states:
AK, AL, AR, AZ, CT, DE, FL, GA, IA, ID, IN, KS, KY, LA, MD, ME, MI, MN, MO, MS, MT, NC, ND, NE, NH, NM, NV, OH, OK, PA, SC, SD, TN, TX, UT, VA, VT, WI, WV, or WY

W-2 candidates only, H-1B sponsorship not available

Responsibilities

Responsibilities include, but are not limited to:

Work with a Data Scientist or manager to understand a well-defined business problem and translate it into an analytical or modeling approach.
Gather, query, clean, and explore data needed to support your project.
Conduct exploratory data analysis to identify patterns, relationships, and potential data quality issues.
Apply appropriate statistical or machine learning techniques to solve a defined problem.
Evaluate model or analytical results using appropriate metrics and validation techniques.
Create clear visualizations that help technical audiences understand your findings.
Write clean, logical, readable, and well-documented Python and SQL code.
Communicate project progress, challenges, assumptions, and results to your manager and other Data Scientists.
Ask questions and seek clarification when requirements, data, or methodology are unclear.
Incorporate feedback from other Data Scientists and iterate on your work.
Present your approach, findings, and recommendations at the conclusion of your project.

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Data Science Summer Intern

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Data Science Summer Intern

Lititz, PA Remote

Entry Level

Internship

W-2 candidates only, H-1B sponsorship not available

Responsibilities

Responsibilities include, but are not limited to:

Work with a Data Scientist or manager to understand a well-defined business problem and translate it into an analytical or modeling approach.
Gather, query, clean, and explore data needed to support your project.
Conduct exploratory data analysis to identify patterns, relationships, and potential data quality issues.
Apply appropriate statistical or machine learning techniques to solve a defined problem.
Evaluate model or analytical results using appropriate metrics and validation techniques.
Create clear visualizations that help technical audiences understand your findings.
Write clean, logical, readable, and well-documented Python and SQL code.
Communicate project progress, challenges, assumptions, and results to your manager and other Data Scientists.
Ask questions and seek clarification when requirements, data, or methodology are unclear.
Incorporate feedback from other Data Scientists and iterate on your work.
Present your approach, findings, and recommendations at the conclusion of your project.

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