Graduate Data Science Intern

Perry Ellis International is seeking a highly motivated and talented Master's level graduate student to join our Data Science team as an Intern. This is an exciting opportunity to contribute to real-world projects, gain hands-on experience in a dynamic environment, and learn from experienced data scientists. The ideal candidate will possess a strong foundation in statistical modeling, machine learning, and programming, and be passionate about leveraging data to solve complex business problems.

Responsibilities:

• Collaborate with senior data scientists and business stakeholders to understand project requirements and translate them into actionable data science solutions.
• Develop and implement statistical models and machine learning algorithms to address specific business challenges.
• Perform data cleaning, preprocessing, and feature engineering to prepare data for analysis and modeling.
• Conduct exploratory data analysis (EDA) to identify trends, patterns, and insights within data.
• Evaluate model performance using appropriate metrics and techniques, and iterate on models to improve accuracy and efficiency.  
• Communicate findings and insights effectively through visualizations, presentations, and written reports.
• Contribute to the development and improvement of data science best practices and tools within the team.
• Stay up-to-date with the latest advancements in data science and machine learning.
• Participate in team meetings and contribute to a collaborative and supportive work environment.  

Qualifications:

• Currently pursuing a Master's degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
• Strong understanding of statistical modeling concepts, including regression, classification, and clustering techniques.
• Proficiency in machine learning algorithms and their applications.
• Experience with programming languages such as Python or R, and relevant data science libraries (e.g., Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch).
• Familiarity with data visualization tools and techniques (e.g., Matplotlib, Seaborn, Tableau).
• Experience with data cleaning, preprocessing, and feature engineering.
• Strong analytical and problem-solving skills.
• Excellent communication and presentation skills, with the ability to explain complex technical concepts to both technical and non-technical audiences.  
• Ability to work independently and as part of a team.  
• Experience with cloud computing platforms (e.g., AWS, Azure, GCP) is a plus.
• Experience with big data technologies (e.g., Spark, Hadoop) is a plus.
• Experience with specific business domains (e.g., finance, healthcare, e-commerce) is a plus.

Benefits:

• Gain hands-on experience working on real-world data science projects.
• Opportunity to learn from experienced data scientists and mentors.
• Exposure to cutting-edge technologies and tools.
• Collaborative and supportive work environment.