Data Scientist Brag Document Example
Q1 2025
Built a predictive model to identify high-churn risk users
Date: January 22, 2025
Company: Offline
Tags: Predictive Modeling, Retention, Machine Learning, Big
Metrics:
Description:
Developed and deployed a churn prediction model using product usage, behavior patterns, and historical retention signals. Shared insights with Product and Growth to inform targeted interventions.
Cleaned and restructured event-tracking schema for better analysis
Date: February 14, 2025
Company: Offline
Tags: Data Engineering, Tracking, Data Quality, Medium
Metrics:
Description:
Audited the entire event taxonomy, fixed duplicate and mislabeled events, and partnered with engineering to standardize naming conventions.
Analyzed onboarding behavior to uncover activation predictors
Date: March 6, 2025
Company: Offline
Tags: Behavioral Analysis, Insights, Product Analytics, Medium
Metrics:
Description:
Performed deep behavioral clustering to determine which early actions correlated most strongly with long-term engagement. Insights shaped Q2 UX improvements.
Q2 2025
Built and deployed recommendation engine for in-app feature suggestions
Date: April 18, 2025
Company: Offline
Tags: Recommendations, Machine Learning, Personalization, Big
Metrics:
Description:
Used collaborative filtering and usage-based signals to suggest relevant features to users. Improved personalization across the product.
Developed forecasting models for ARR and product usage
Date: May 20, 2025
Company: Offline
Tags: Forecasting, Time Series, Predictive Analytics, Medium
Metrics:
Description:
Built time-series models that incorporated historical performance and seasonality. Provided visibility to Finance, Product, and Marketing.
Created a reusable experimentation analysis toolkit
Date: June 6, 2025
Company: Offline
Tags: Experimentation, Statistics, Tooling, Small
Metrics:
Description:
Developed Python notebooks and SQL templates for analyzing lift, significance, power, and segmentation results. Improved pace and reliability of experiment insights.
Q3 2025
Built customer lifetime value (LTV) model for revenue forecasting
Date: July 15, 2025
Company: Offline
Tags: LTV Modeling, Revenue Analytics, Machine Learning, Big
Metrics:
Description:
Created cohort-based and probabilistic models to estimate future value. Informed pricing discussions and strategic planning for Q4 and beyond.
Performed clustering to identify behavioral user segments
Date: August 21, 2025
Company: Offline
Tags: Clustering, Segmentation, ML, Medium
Metrics:
Description:
Used unsupervised learning to group users by feature usage, activation signals, and retention behavior. Shared personas with PMM, Product, and Growth.
Built anomaly detection system for product KPIs
Date: September 10, 2025
Company: Offline
Tags: Monitoring, Automation, Machine Learning, Medium
Metrics:
Description:
Implemented anomaly detection using statistical and ML-based techniques to catch metric spikes and drops early.
Q4 2025
Developed ML-powered search ranking improvements
Date: October 16, 2025
Company: Offline
Tags: Search, Ranking Models, Machine Learning, Big
Metrics:
Description:
Designed and tested ranking algorithms using click-through behavior, session patterns, and relevancy scores. Improved search experience significantly.
Optimized ETL pipelines for faster data processing
Date: November 14, 2025
Company: Offline
Tags: ETL, Data Engineering, Optimization, Medium
Metrics:
Description:
Refactored transformations, improved indexing, added caching, and introduced monitoring to make pipelines more reliable and cost-efficient.
Created 2026 data science strategy and research roadmap
Date: December 5, 2025
Company: Offline
Tags: Strategy, Data Science Leadership, Roadmapping, Beyond
Metrics:
Description:
Outlined priorities across modeling, data infrastructure, ML ops, experimentation, and analytics education. Established goals for improving prediction accuracy and data visibility.
Kudos
“Your churn model gave us clarity we never had before.”
From: Priya Shah — Director of Product
Date: January 30, 2025
Impact: Helped Product and CS take action on at-risk groups early.
“The recommendation engine changed how users discover new features.”
From: Hannah Cole — VP of Product
Date: April 29, 2025
Impact: Improved engagement and long-term retention metrics.
“Your LTV model reshaped our revenue planning for the better.”
From: Daniel Brooks — CEO
Date: July 30, 2025
Impact: Supported more accurate forecasting and strategic goal-setting.
“The pipeline optimization work saved us serious compute costs.”
From: Nick Ramirez — Senior Data Engineer
Date: November 21, 2025
Impact: Improved system performance and reduced monthly overhead.
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