Data Scientist resume example

• Used SQL and Python to study seller retention, separating new-seller cohorts from established shops and checking seasonality; reported uncertainty rather than treating a correlation as a causal effect.
• Built Looker dashboards from the research dataset with documented cohort definitions and drill-through samples; reconciled dashboard totals to the source queries before sharing recommendations.
• Separated training and evaluation data by time and customer, inspected leakage in derived features, and compared model performance across cohorts before recommending an experiment.
• Built a Python machine learning baseline for repeat-purchase prediction, split observations by customer and time, and compared calibration and recall with a simple statistical baseline before presenting the model recommendation.
• Prepared an experiment readout with confidence intervals, cohort-level diagnostics and a data visualization of the pre-test trend; checked sample-ratio mismatch and explained when the evidence did not justify rollout.
• Owned a forecasting evaluation using time-based splits in Python, reducing holdout error from 19% to 15% against the same seasonal baseline and recording cohort failures.
• Published a Tableau evaluation dashboard using SQL-validated inputs, resolving differences between data-pipeline totals and the statistics presented to decision makers.

• Built a demand forecast with promotion and holiday effects for 26 regions. Kept the improvement stable through the follow-up review.
• Investigated a discrepancy between a published metric and its source records, traced the transformation that changed the population, and corrected the calculation with a reproducible query.
• Compared the last successful data refresh with a failed run, separated missing source data from transformation errors, and reran only the affected interval. Kept the original query and corrected result together for review.

• Reconciled three conflicting definitions of active supply into one governed metric
• Generated synthetic source records with duplicates, late arrivals and corrected values; wrote assertions for row counts and key uniqueness, and recorded the expected effect of each case on the reported metric.
• Compared the analytical output with a manually calculated reference table, traced differences to a transformation step, and retained a data dictionary and rerun instructions alongside the corrected query.
• Owned the synthetic-data validation using a manually calculated reference, resolved duplicate-key inflation and completed a notebook that reproduces the corrected totals.

Relevant coursework: Algorithms, operating systems, databases, computer networks

• Published an independent methods note using reproducible queries, explaining the data grain, excluded records and sensitivity of the result to a changed denominator.