Ashmal Rahman

Spearheaded a 3-member analytics team to deliver enterprise BI and automation solutions for a major US Media & Entertainment client, overseeing end-to-end execution across reconciliation, fraud analytics, and financial reporting workstreams., Engineered Capacity Planning Machine Learning forecasting models utilizing SARIMA and Prophet to accurately predict future capacity needs, enhancing labor utilization by 15% and mitigating fulfillment delays., Automated product launch approval workflows through Power Automate, liberating over 100 man-hours monthly and expediting cross-departmental coordination., Instituted data governance standards and a KPI taxonomy across reporting surfaces, introducing semantic layer design to guarantee consistent metric definitions., Architected and developed a Snowflake + dbt warehouse from inception, designing raw, staging, and mart layers, building models and tests, and integrating with Power BI, which diminished ad-hoc SQL requests by 40% and established a unified source of truth., Directed the Databricks migration to modernize data infrastructure, improving pipeline reliability and reducing unreconciled financial exposure from approximately $55 million to $150,000.

Spearheaded a 3-member analytics team to deliver enterprise BI and automation solutions for a major US Media & Entertainment client, overseeing end-to-end execution across reconciliation, fraud analytics, and financial reporting workstreams.
Engineered Capacity Planning Machine Learning forecasting models utilizing SARIMA and Prophet to accurately predict future capacity needs, enhancing labor utilization by 15% and mitigating fulfillment delays.
Automated product launch approval workflows through Power Automate, liberating over 100 man-hours monthly and expediting cross-departmental coordination.
Instituted data governance standards and a KPI taxonomy across reporting surfaces, introducing semantic layer design to guarantee consistent metric definitions.
Architected and developed a Snowflake + dbt warehouse from inception, designing raw, staging, and mart layers, building models and tests, and integrating with Power BI, which diminished ad-hoc SQL requests by 40% and established a unified source of truth.
Directed the Databricks migration to modernize data infrastructure, improving pipeline reliability and reducing unreconciled financial exposure from approximately $55 million to $150,000.

Directed analytics for a major cosmetics client, delivering comprehensive insights into sales, pricing, operations, and product performance utilizing Python and Power BI., Formulated multivariate price elasticity models to optimize SKU pricing strategies, achieving a 10% increase in gross margin with minimal impact on volume., Designed and implemented A/B tests for pricing and promotions, applying two-proportion z-tests to guide decisions and boost conversion rates by 12% and Average Order Value (AOV) by 8%., Assessed new product performance through cannibalization and halo-effect analyses to precisely quantify organic versus incremental growth., Constructed a logistics Service Level Agreement (SLA) dashboard to identify breach patterns and chargeable fee trends, thereby reinforcing cost recovery and operational transparency., Developed and maintained Azure Data Factory (ADF) pipelines to orchestrate data ingestion from diverse source systems into Azure Synapse Analytics, incorporating automated monitoring, alerting, and retry mechanisms that reduced pipeline failure impact by approximately 60%., Built end-to-end Power BI and Tableau dashboards to track churn, AOV, regional trends, and SKU profitability, thereby accelerating decision-making cycles for leadership.

Directed analytics for a major cosmetics client, delivering comprehensive insights into sales, pricing, operations, and product performance utilizing Python and Power BI.
Formulated multivariate price elasticity models to optimize SKU pricing strategies, achieving a 10% increase in gross margin with minimal impact on volume.
Designed and implemented A/B tests for pricing and promotions, applying two-proportion z-tests to guide decisions and boost conversion rates by 12% and Average Order Value (AOV) by 8%.
Assessed new product performance through cannibalization and halo-effect analyses to precisely quantify organic versus incremental growth.
Constructed a logistics Service Level Agreement (SLA) dashboard to identify breach patterns and chargeable fee trends, thereby reinforcing cost recovery and operational transparency.
Developed and maintained Azure Data Factory (ADF) pipelines to orchestrate data ingestion from diverse source systems into Azure Synapse Analytics, incorporating automated monitoring, alerting, and retry mechanisms that reduced pipeline failure impact by approximately 60%.
Built end-to-end Power BI and Tableau dashboards to track churn, AOV, regional trends, and SKU profitability, thereby accelerating decision-making cycles for leadership.

Generated Budget Variance reports (Sales, COGS, Expenses, EBITDA vs. Prior Year/Budget), empowering leadership to address financial deviations within a 24-hour timeframe.
Utilized Google Analytics 4 to analyze user funnels, Customer Acquisition Cost (CAC), and channel Return on Investment (ROI), facilitating data-informed budget reallocation across paid, organic, and referral channels.
Performed price-volume-mix analysis to deconstruct revenue growth for dairy products, identifying contributions from pricing adjustments, demand fluctuations, and product mix shifts to inform pricing and portfolio strategy.
Streamlined manual reporting processes by developing Excel macros (VBA) and Python scripts, reducing the team's workload by 25%.