Amazon

• Designed, developed and deployed a scalable search algorithm on AWS using Sentence-BERT and ANNOY index, enhancing the user experience which led to an increase in user engagement by 24%. The project is deployed on AWS using Docker containers, with a Flask API serving as the interface for users to interact with the system.
• Developed a Lookalike model using a Locality Sensitive Handling algorithm based on online digital ad campaign data, leading to an increase of 30% in click through rate.
• Developed GARCH and ARCH time series models to forecast customer agent demand across Amazon call centers in 7 countries, leading to a reduction in the overall expenditure by 12%.
• Developed and executed the data pipeline architecture for 5 Financial dashboards, enabling monthly reporting and streamlining product development business processes, saving approximately 10 hours per week per team.
• Designed and deployed a data visualization dashboard for Weekly Business Reviews, providing stakeholders with visually impactful and actionable insights and thereby increasing executive team's visibility into project metrics.

Business Intelligence Engineer, Data Engineering, MLOps and Forecasting

U.S. Bank

• Spearheaded the creation and implementation of key business metrics to monitor the operational efficiency and customer experience of U.S. Bank's Authentication modules, resulting in a 15% increase in self-service adoption and a 17% reduction in average handling time for customer inquiries.
• Developed Executive Level dashboards and standardized reporting capabilities, providing real-time insights into various US Bank products, marketing funnels, and business KPIs.

Assistant Vice President - Data Science

Data Analytics

University of Minnesota - Carlson School of Management

Client: A Leading Customer Experience Intelligence Company

• Devised a mechanism to filter and aggregate call center calls using the change in emotions of a customer and the call metadata, reducing 800 calls down to 6 per agent, decreasing the agent performance evaluation time by up to 50%.
• Trained an LSTM neural network to capture the change of emotions of a customer in a call using audio data.
• Leveraged NLP techniques and cosine similarity on call transcripts to indicate agent-customer conversational mirroring.

 Client: Hennepin County and Second Harvest Heartland

• Identified penetration rate of SNAP (Supplemental Nutrition Assistance Program) using R and Tableau by exploring socio-economic and health indicators through cluster analysis and visualization with PCA.
• Presented area-specific recommendations based on local factors, which could lead to a 25% increase in usage of SNAP.
• Created a Tableau dashboard visualizing the location-based penetration rate of SNAP to identify areas needing assistance.

Analytics Student Consultant

Abhaya Anant Foundation

• Conducted exploratory analysis on Indian Census and used anomaly detection techniques to identify areas in acute need of assistance

• Procured funding of $65K from the government of India and used it to provide nutrition assistance to 211 households, education funding for underprovided children, and skill development workshops for the unemployed and unskilled.

• Created weekly updated dashboards on Tableau for location and demography-based analysis and campaign planning for all the social events conducted by the NGO.

Co-Founder, Director

Yapsody India Pvt. Ltd.

• Designed and implemented a microservice architecture for the client’s web application and integrated the code base of the application. The software application generated a revenue of $55K in the first 3 months of going into production.

• Built out the data and reporting infrastructure from the ground up using Tableau and SQL to provide real-time insights into the product, marketing funnels, and business KPIs.

• Created a deep learning-powered autoencoder using Python (TensorFlow and Keras) to significantly enhance the quality of images on the application, leading to an increase of 25% in website conversion rate.

Software Developer

Cybage Software

• Reduced the page loading times of the client’s application by 40% by implementing Redis (data caching), optimizing the APIs and the database for higher performance by SQL query optimization and use of table indexing, stored procedures,
and triggers.

• Contributed to the design and implementation of the database architecture of the application, developing conceptual ERDs (Entity Relationship Diagrams) and their translation to physical ERDs.

• Implemented and configured a data pipeline through AWS using Lambda for injecting application user review data from S3 and DynamoDB storage solutions to services such as Athena and Comprehend

• Developed a content-based journal recommendation system for Research Journals based on users existing preferences.

• Optimized the user interface for all resolutions (mobiles, tablets, and desktop browsers) across different platforms (Android, IOS, and Windows PC)

Software Engineer

With more than 7 years of demonstrated history in designing and implementing robust, scalable, and maintainable machine learning solutions, and ETL pipelines, I am passionate about data-driven solutions that lead to actionable insights and business decisions.

Github: https://github.com/shaleenswarup

Tools and Technologies: SQL, NoSQL, Python, JavaScript, Tableau Quicksight, Hadoop, Spark, Kafka, Airflow, Databricks, TensorFlow (TXF), Docker, Kubernetes, Docker, Bash, dbt, Git

Cloud Platforms: Amazon Web Services (AWS) - EC2, S3, AWS Redshift, DynamoDB, Glue, Athena, EMR, Lambda, SQS, SNS, Kinesis; Azure -Azure Synapse, Azure Data Factory, Azure Databricks

Databases: Redshift, Azure Synapse, Cassandra, MongoDB, Postgres, MySQL

Techniques: Predictive Modelling, Exploratory Analysis, Statistical Analysis, Time Series Forecasting, A/B Testing

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