Ashwini Choudhary

• Built Tableau and Metabase dashboards tracking 15+ sales, inventory, and customer KPIs, cutting reporting time by 30% and delivering real‑time visibility to marketing, operations, and leadership. • Analysed 50,000+ customer transactions (SQL, Excel, Python) to identify a high‑value segment driving 45% of sales, directly shaping targeted retention campaigns that lifted conversion by 18% and repeat purchase frequency. • Evaluated sales and margin data across 200+ products, pinpointing top categories and margin improvement opportunities; the resulting assortment rebalancing contributed to a 12% category revenue uplift. • Monitored inventory turnover and seasonal demand patterns, reducing stock‑out risk by 15% and excess inventory by 10% through dynamic replenishment adjustments. • Partnered cross‑functionally with Marketing, Inventory, and Pricing to turn complex data into actionable strategies, improving campaign precision, stock distribution, and pricing—raising category margins by 8%.

• Analysed 5M+ prospect records using SQL and Python, surfacing high‑value lead segments for the sales team. Built Tableau visualisations that made prioritisation instant, helping reps focus on the prospects most likely to convert and improving pipeline quality. • Developed self‑service Tableau dashboards and analytics‑ready datasets, giving sales and marketing stakeholders direct access to lead performance metrics. This cut ad‑hoc reporting requests by 30% and freed up analyst time for deeper investigations. • Automated data preparation workflows with dbt and Airflow, ensuring consistent, clean data flowed into Excel and Power BI reports. Manual effort dropped by 30% while data reliability improved, making daily reporting faster and more trustworthy. • Partnered with sales and growth teams to turn prospect data into actionable strategies, including Excel‑based scenario models that shaped targeting decisions and helped grow the qualified pipeline.

• Built Grafana and Tableau dashboards to monitor machine learning model performance and operational KPIs, giving research and engineering teams real‑time visibility into experiment health. This cut troubleshooting time and helped keep critical experiments on track. • Performed time‑series analysis using Python to identify performance trends and anomalies across experimental datasets, directly supporting model optimization using Dbt and improving experiment reliability. • Automated data collection using Airflow, validation, and preprocessing workflows, reducing manual effort by 30% and delivering cleaner, analysis‑ready datasets that sped up reporting and research cycles. • Analysed experimental results and translated findings into clear, actionable insights for research teams, enabling faster, evidence‑based decisions on model iteration, resource allocation, and experimental planning.

• Analyzed SAP S/4HANA sales, profit, and emissions data using SQL and Python to uncover trends that shaped strategic business decisions. Drove insights into regional profitability drivers and sustainability hotspots, directly informing resource allocation and investment planning. • Built Tableau and Metabase dashboards for AES LATAM, giving leadership real‑time visibility into 20+ KPIs including sales, profitability, and power plant operations. These dashboards replaced static reports, significantly speeding up data‑driven decision‑making across the region. • Applied time‑series forecasting to CO₂ emissions data to project long‑term trends, turning complex environmental data into clear forward‑looking outlooks using Dbt that guided sustainability planning and operational forecasting. • Delivered clear, actionable insights through executive reports and dashboards, translating complex datasets into strategic recommendations that supported plant expansion, emission reduction targets, and cross‑functional sustainability initiatives.