Sindhu Allam Email & Phone Number

United States

Supported a leading automotive parts retailer operating over 6,400 stores across North America, with 50 distribution centers and over 323,000 SKUs generating $11Bn in annual sales (~$25Mn daily sales, ~800K daily transactions)• Analyzed sales and promotional campaigns data using clustering techniques to identify customer segments, revenue driving factors to support decisions resulting in net sales increase by 1.3% to $3.4Bn. Implemented machine learnings models leveraging marketing and non-marketing data across web, search, social, email to identify customer buying behavior and improve conversion rates, user experiences on ecommerce sites. • Leveraged multiple analytical tools & data pipelines to extract, model and analyze billions of SKU records of inventory, sales, customer data and extract key business insights. Built high visibility intra-day reports to present findings primarily referred by c-suite and other functional leaders. Optimized snowflake SQL query execution with stored procedures allowing Power BI reports to achieve sub-one minute response time with data query• Leveraged time series and historical sales data to perform feature extraction and pattern recognition to build regression models with seasonality and demand forecasting to predict future demand & support pricing strategies. Built dashboards to track seasonality of product category to support business & promotional planning• Developed workflows to identify duplicate records and clean sales data across various databases. Collected, cleansed, supported modelling and analyses of structured & unstructured data used for major business initiatives.

Cincinnati Metropolitan Area

Provided analytics support to an industry-leading clinical asset management company leveraging extensive data (on 1.7+ million medical devices worth over $15Bn) to help healthcare providers transform their clinical assets strategy driving reductions in operational expenses, optimizing capital spend and maximizing resources for patient care. • Supported development of benchmarking scores and utilization index using regression analysis to identify surplus and underutilized devices (ventilators), provide relocation recommendations to sites with higher patient demand. Resulted in relocating 800+ devices, with 25% Cap-Ex avoidance & 15% Op-Ex savings by avoiding new purchases• Built ML models using equipment data to analyze asset inventory, utilization, defect rates and calculate useful life of devices/recommend replacement. Generated insights resulting in replacement of 10% inventory (worth $35M)• Performed univariate and bivariate analysis on equipment data to understand utilization trends and operational expenses. Supported development of defect prediction models leveraging historical device data logs (CT and MRI scanners), defect status data, device make/model/description to predict equipment expenses and future needs • Built regression models to calculate equipment sale prices leveraging benchmarking and Market Value data for clinical assets to maximize return on capital investment. Resulting in 20% assets sold above OEM trade-in value.

Postgraduate, Applied Data Science And Machine Learning

Activities and Societies: Machine Learning: - Supervised Learning - Unsupervised Learning - Ensemble Techniques - Recommendation Systems.

Master Of Science - Ms, Computer Science, 3.4/4

Bachelor Of Engineering - Be, Electronics And Communications Engineering, 8.7/10