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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