Senior Data Analyst with experience in data analysis, machine learning, and business impact analysis across roles at Toptal and Intertec.io. Skilled in SQL, Python, R, Tableau, Power BI, cloud platforms, and statistical …
Senior Data Analyst with experience in data analysis, machine learning, and business impact analysis across roles at Toptal and Intertec.io. Skilled in SQL, Python, R, Tableau, Power BI, cloud platforms, and statistical techniques including A/B testing, cluster analysis, propensity modeling, and cohort analysis.
• Spearheaded a data-driven benchmarking analysis to optimize operational costs for a leading energy company with a comprehensive fleet, identifying $800K in potential annual savings through 20% fuel efficiency improvements.
• Analyzed IoT telematics data (fuel consumption, distance traveled, driving behaviors) from Pembina's 800+ vehicle fleet using a suite of
data analysis tools, including Python, Pandas, scikit-learn, and TensorFlow. This analysis enabled the identification of a 24% correlation between aggressive driving and lower fuel efficiency compared to the benchmark, established through cluster analysis techniques.
• Established relevant industry benchmarks by employing cluster analysis techniques to identify 15 companies with similar fleet characteristics for comparative analysis.
• Proposed a targeted driver coaching program utilizing real-time telematics data monitoring and in-vehicle feedback mechanisms to reduce harsh driving incidents, optimize fuel usage, and achieve benchmark-level efficiency.
Spearheaded a data-driven benchmarking analysis to optimize operational costs for a leading energy company with a comprehensive fleet, identifying $800K in potential annual savings through 20% fuel efficiency improvements.
Analyzed IoT telematics data (fuel consumption, distance traveled, driving behaviors) from Pembina's 800+ vehicle fleet using Python, Pandas, scikit-learn, and TensorFlow.
Identified a 24% correlation between aggressive driving and lower fuel efficiency compared to the benchmark, established through cluster analysis techniques.
Established relevant industry benchmarks by employing cluster analysis techniques to identify 15 companies with similar fleet characteristics for comparative analysis.
Proposed a targeted driver coaching program utilizing real-time telematics data monitoring and in-vehicle feedback mechanisms to reduce harsh driving incidents, optimize fuel usage, and achieve benchmark-level efficiency.
Developed and deployed a Random Forest Classifier model achieving 90.33% accuracy in predicting customer churn for a gas and power company.
Informed targeted discount strategies and maximizing revenue while maintaining customer satisfaction.
Championed business impact analysis, optimizing revenue with a 20% discount strategy at a cutoff probability of 0.5.
Resulted in a 30% revenue increase and significant bottomline growth for the organization.
Engineered key features and implemented robust evaluation framework using precision, recall, and AUC.
Optimized and ensured reliability of churn model, enabling effective identification of at-risk customers.
Implemented A/B testing for promotional strategies using customer segmentation and uplift testing.
Resulted in a 7.3% increase in sales and a 10.4% increase in transactions over control stores.
Increased pre-peak season sales by 15% through data-driven marketing.
Analyzed 52 weeks of sales data with SQL and Tableau to identify customer segments and develop targeted promotions.
Maximized post-trial sales by $630/week and transactions by 70 through data-driven trial campaign selection.
Employed advanced statistical analysis techniques like propensity modeling and cohort analysis to identify high-performing campaigns from historical data.
Resulted in a 10% improvement in customer acquisition efficiency.
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