Data Science Intern

• Support the development and evaluation of models including regression time-series and other statistical or machine learning approaches with attention to predictive performance stability and interpretability.
• Prepare and explore data using Python and SQL; perform data-quality checks feature construction descriptive analysis and visualization to understand modeling inputs and outcomes.
• Apply core model-validation techniques such as train/validation/test splits cross-validation baseline comparisons and appropriate performance metrics.
• Investigate common statistical issues including multicollinearity overfitting residual patterns autocorrelation heteroskedasticity and unstable coefficients with guidance from senior team members.
• Test and compare reasonable modeling choices such as feature transformations regularization settings and model specifications and summarize how these choices affect model results.
• Interpret model outputs and connect technical findings to practical marketing or business questions while clearly stating assumptions and limitations.
• Contribute to reproducible analytical workflows for model training validation sensitivity checks and result comparison.
• Write clear Python and SQL code and communicate methods findings assumptions and open questions in a structured and understandable way.

• Strong foundation in statistics and regression: understanding of linear regression key model assumptions coefficient interpretation regularization concepts and basic statistical inference.
• Solid quantitative fundamentals in probability statistics and linear algebra; familiarity with calculus or optimization concepts is helpful.
• Working knowledge of Python for data analysis and modeling including common data-science libraries; basic to intermediate SQL skills for data extraction and transformation.
• Understanding of model evaluation: training versus validation data cross-validation common regression metrics overfitting and the importance of out-of-sample performance.
• Ability to reason through modeling problems: investigate unexpected results form hypotheses about root causes test alternatives and explain conclusions using evidence.
• Clear communication skills: ability to explain analytical methods assumptions results and limitations to technical teammates and learn from feedback.
• Currently pursuing a degree in statistics computer science data science machine learning applied mathematics econometrics operations research or a closely related quantitative field.

• Coursework research or project experience using regression time-series analysis statistical modeling or machine learning.
• Exposure to Marketing Mix Modeling (MMM) marketing analytics attribution or other measurement problems.
• Basic understanding of concepts such as adstock saturation incremental impact ROI or response curves.
• Familiarity with A/B testing causal inference simulation sensitivity analysis or confidence intervals.
• Experience with Python libraries such as pandas NumPy statsmodels scikit-learn SciPy or similar tools.
• Previous internship research assistantship academic project or independent project involving real-world data is a plus.

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