Zain Khan

Developed advanced data models and implemented statistical techniques to address complex business challenges, resulting in a 20% reduction in operational inefficiencies.

• Led a project analyzing customer churn by creating a predictive model using logistic regression. The model identified high-risk customer segments, enabling targeted retention strategies that reduced churn by 15% within six months.

Analyzed large datasets using SQL and Python to uncover trends, patterns, and insights that informed strategic decision-making across departments.

• Developed a SQL-based dashboard for the sales team that highlighted daily, weekly, and monthly sales trends, significantly improving forecasting accuracy and aiding in inventory management.

Built predictive models using machine learning algorithms, including regression, decision trees, and random forests, to forecast key metrics like revenue and customer acquisition.

• Designed a random forest model that accurately predicted monthly revenue for a new product line, helping management make data-driven inventory and marketing decisions.

Led A/B testing experiments to assess the effectiveness of different marketing strategies, providing clear insights that boosted campaign ROI by 25%.

• Ran A/B tests on two email marketing campaigns, analyzing user engagement metrics and conversion rates. Insights from this experiment informed a restructured email approach, resulting in a 10% increase in click-through rates.

Automated reporting processes by developing custom SQL queries and Python scripts, reducing time spent on data retrieval by 28%.

• Built an automated monthly sales report that extracted data from multiple sources, cutting down manual reporting time from two days to three hours.

Presented actionable insights from data analysis, which led to a 15% increase in budget allocations to high-performing sectors.

• Analyzed customer segmentation data and presented findings to leadership, demonstrating potential revenue uplift in key markets. This led to expanded marketing efforts in these segments.

Collaborated with IT to design optimized database structures for managing large volumes of data, reducing data retrieval times by 30%.

• Worked with the IT team to restructure the data storage system for product metrics, improving query efficiency and reporting speed for the business intelligence team.

Developed a comprehensive tracking system using Excel and Tableau to monitor and analyze client fitness progress, enhancing reporting accuracy and usability for trainers and clients.

• Created a weekly progress dashboard in Tableau that displayed key metrics such as weight, body composition, and workout intensity, providing clients with clear visual insights into their progress.

Employed SQL to manage client data and Python for in-depth analysis, which led to a 20% improvement in client retention and satisfaction by delivering data-backed, personalized feedback.

• Leveraged SQL to structure and store client data, and used Python to analyze patterns in workout adherence and improvement rates, allowing trainers to adjust client plans for better engagement.

Designed visual progress reports and customized fitness plans based on client-specific data analysis, helping clients stay motivated and focused on their goals.

• Generated monthly personalized fitness reports in Tableau that highlighted individual achievements and recommended areas of focus, leading to a 25% increase in client satisfaction scores.

Analyzed healthcare data to identify trends and propose process improvements, which led to a 10% decrease in operational delays.

• Conducted an analysis of patient wait times and identified bottlenecks in service delivery. Suggested workflow adjustments that reduced wait times by 15%.

Conducted gap analysis between current workflows and desired outcomes, assisting in optimizing clinical processes.

• Evaluated existing clinical data handling processes, leading to the implementation of a streamlined workflow that improved patient data accuracy.