VELLORE JAYAKUMAR SREENIVASAN

As a proficient Manager having strong knowledge of data science specialized in Data Engineering, Data Analytics and machine learning (ML), led various Data project for multiple products (Funnow, Eatigo, Tableapp, Niceday), contributing to strategic decision-making and business optimization. In this capacity:
▪ Demonstrated leadership in managing data teams comprising BI professionals, data engineers, and data scientists, ensuring cohesive collaboration and efficient project delivery.
▪ Collaborated with diverse stakeholders to conceptualize and implement comprehensive technical solutions aligned with project requirements and industry best practices, fostering innovation and excellence.
▪ Implemented strategies to optimize tech and data infrastructure, effectively reducing infrastructure spending while aligning development activities with production traffic demands.
▪ Orchestrated seamless collaboration between product engineering and data team, establishing clear communication channels to facilitate smooth planning, execution, and rollout of features, enhancing team productivity and cohesion.
▪ Collaborated closely with finance, sales, and marketing teams to gather requirements, validate model outputs, and refine forecasting methodologies, ensuring alignment with business objectives.
▪ Establishing and enforcing Data Governance and Quality standards by ensuring the integrity, accuracy, and reliability of data sources and datasets and implementing data quality assurance processes and by monitoring and notifying inconsistency.
▪ Engineered machine learning (ML) models to generate personalized recommendations utilizing techniques like collaborative filtering and content-based filtering.
▪ Conducted comprehensive feature engineering to extract relevant predictors and optimize model performance, drawing on deep domain expertise and data-driven insights.
▪ Implemented robust A/B testing methodologies to assess the impact of new features and business strategies on revenue performance, ensuring reliable and actionable forecasts for strategic planning & facilitating data-driven decision-making.
▪ Experienced in productionalising ML models with automated scheduled training and streamlined CI/CD pipelines, ensuring efficient data processing, model deployment.
▪ Designing and developing advanced analytics solutions, including predictive models, dashboards, and reports.
▪ Interpreting data insights and translating them into actionable recommendations for the business.
▪ Manage data team for multiple products and Creating a comprehensive roadmap and ensuring clear communication and alignment of focus areas through regular updates and information sharing with team members and stakeholders

• Technology Stack: Spark, AWS, GCP, Glue, Athena, Lambda, Redshift, BigQuery
• Analytics/Reporting: Redash, GA, Firebase Analytics, Metabase, Design & develop data pipelines and implemented data warehouses using a variety of AWS services
• Closely coordinated with engineering, product, finance, sales and operation teams to understand their requirements
• Collect data from diverse platforms and created optimized data models and ETL & ELT workflows on the data warehouse system
• Migrated resource-intensive batch jobs from core applications to big data workflows
• Built CI/CD pipelines to ensure smooth deployment on various environments and setup notifications on deployment failures
• Built prototypes and conducted feasibility studies (cost, effort, and risk) for new AWS services promising effective processing Collection data from Merchant CRM (Salesforce & Zoho) & user CRM (Braze & CleverTap) and analyzing the reports Developed ML models for customer segmentation and merchant scoring
• Develop KPI dashboards and dashboards for concerned teams on monthly, weekly, week on week and month on month basis for detailed insights
• Planned and executed the data migration for the switchover from Braze to CleverTap and from Salesforce to Zoho, ensuring a seamless transition and minimal disruption to business operation
• KPI reports include Funnel tracking analysis, user behavior analysis for Product team
• Merchant inventory, BD/AM/CL commission report for Sales team
• Booking report, Ops agent performance report Operations
• Booking revenue, revenue forecasting, collected revenue reports for Finance and Customer Lifetime Value, user acquisition cost analysis for marketing team

Project Description:

• LTA-DE: Developed a new scalable and cost-efficient system to detect and prevent fraudulent transactions using ML models, and migrate from legacy using proprietary software.
• Data Hub: Build data lake system to collect data from multiple data sources, which is transformed, modelled and persisted onto data warehouse system, facilitating complex queries and analytics

Responsibilities:

• Analyzed the legacy Decision Engine (DE) design, functionality, and business use cases using IBM MQ, Drools
• Led requirement gathering. Estimate resource, time and costs and produced project quotations for the new design
• Identified various components suitable for the new design, benchmarking performance and create a proof of concept (POC)
• Handle Architecture proposal, design, development, of LTA-DE
• Sphere headed the shadow testing, deployment, migration and switch over
• Generated metrics and reports to understand and compare application performance and reliability
• Build data pipelines to collect data from multiple sources to a visa data lake system
• Perform transformation and perform data modelling using fact and dimension tables

Description:
Claims prediction project aims to leverage ML techniques to predict insurance claims by analyzing historical claims data and various influencing factors. The project seeks to enhance the efficiency of claims processing by accurately predicting claims while identifying fraudulent claims

Responsibilities:

• Gather and consolidate data from various sources, ensuring data integrity & completeness
• Perform pre-processing & Feature engineering - cleaning, handling missing values, outlies, inconsistencies & normalization
• Selected and trained various ML algorithms
• Evaluate model performance using F1 score, accuracy, precision, recall, AUC metrics and optimize hyperparameters to improve performance
• Collaborate with infra team to ensure smooth deployment and integration
• Continuously monitor model performance and accuracy to ensure ongoing effectiveness
• Update models with new data to adapt to changing patterns and maintain high predictive accuracy
• Implement feedback loops to incorporate user and system feedback for continuous improvement and training of models

Description:

Finacle Data vault : Building a datawarehouse system using bigdata tech stack for a banking client

Responsibilities

• Analyzed requirements to design and set up a cluster environment.
• Data migration from Oracle DB to HDFS and Hive tables using Sqoop.
• ETL processing using Pig scripts
• Create UDF’s on Hive tables for data processing

• Designed and developed UI menu screens within the module
• Implemented client-side validation, server-side validation, and business logic coding
• Created APIs to handle requests from various channels, service the requests, and send appropriate responses back to the requesting channel
• Deployed the application on the WebSphere application server for production use.