Kumar Navneet

Designed and developed a Card Decisioning AI-based ecosystem to accelerate marketing campaigns for a leading Australian bank.

Responsibilities

• Built an automated system to capture real-time customer responses and integrate model predictions, enabling precise targeting for diverse campaigns.
• Delivered a prediction output layer that empowers campaign teams to execute initiatives more efficiently and effectively.
• Architected and scaled the ecosystem on AWS Cloud with Databricks Lakehouse, ensuring high performance and adaptability across multiple portfolios.
• Played an active role as Solution Architect, designing and implementing new system solutions to meet evolving business needs.
• Acted as Delivery Lead for remediation activities, overseeing migration from legacy systems to modern platforms.

• Managed the Design, Implementation, Integration, Monitoring of application of newly constructed MACT (Motor Accident Claims Tribunal) by Transport Department for providing the compensation to Road Accident Claims Victims.
• IRAD and eDAR implementation in the State of Bihar(First in India) by all stakeholder department - Police, Transport, Health, Road Dept., SLSA / DLSA, MACT, Insurance Company etc.
• Organized and conducted various meeting with detailed presentation on various agenda to Chief Minister, Chief Secretary, Development Commissioner, Additional Secretary of different stakeholder dept. of Bihar on topics such as Hit&Run Compensation Scheme implementation in state, Executive Committee on Road Safety, MACT establishment in state, iRAD/eDAR launch in state.

• Working on the role of IT manager to collaborate and work together will all stakeholders governments department - Police, Transport and different government departments.
• The core responsibility is to integrate ICCC Smart World solutions to all components namely CCTV surveillance, Video analytics, ITMS, ANPR, ATCS, VMS, Data Center, Integration of E-Challan with NIC.
• IT Infrastructure, Networking, Servers, IoT, DC, DR, Web application/Mobile App Development, MIS Reporting ,Procurement, Contracting, Stakeholder, Resource, Risk and Quality Management, Vendor Management, DPR, RFP.

• Manged the team member of size 20 for migrating the LIC Insurance Company solution from existing solution from legacy system like SAS to Cloud based solution - Azure cloud solution.
• Automation, Code Optimisation, conducting the stakeholders meeting to seamlessly migrate and build the new solution by working on the development of best proposed solution.

• Fraud Detection Developed Fraud detection solution for leading bank to predict the transaction to be fraud or non-fraud based on the banking data.
• Responsibilities Collected business requirement and leveraged data from different data sources.
• From the raw data picked the sample data based on the EDA and pattern analysis.
• Applied Logistic Regression as baseline model to obtain the fraud and non-fraud transaction.
• Used Feature engineering to leverage the hidden information from independent metrics and rerun the data with ensemble techniques -Random Forest, LGBM.

• Worked to develop the anomaly detection solution for the supply chain solution on life science data.
• Responsibilities Develop the solution using isolation forest and z-score to identify the anomaly trend in data for end customer.
• Created the pipeline to integrate different data source with the model script and finally storing then to SQL table
• The anomaly classification was being presented to customer in the form of Tableau dashboard.
• Customer Churn Prediction Built a customer churn model which was setup in the Operations team of a leading telecom company to analyse the factors contributing the retention of customer. Model helped business reduce first year churn rate from 34% to 30%.
• Responsibilities Involved in gathering requirements, extracting data from different source files and Databases.
• Unearthed the raw data by performing Exploratory Data Analysis.
• Used Logistic Regression as baseline model to obtain the probabilities of non-churners and churners.
• Applied Feature Engineering to extract information from the independent variables and rerun the model.
• Azure Data Scientist and Azure Data Engineer Building and designing the new ML pipelines using azure machine learning services and azure machine learning studio.
• Responsibilities Working on generating a new Azure ML pipeline to target in elite customers as part of Covid Campaign.
• Created headroom Statistical model pipeline in azure ML by considering the last 6 months customer behavioural history.
• Developed the new data flow pipeline using ADF, Azure databricks, Azure storage, Datalake, Azure SQL DW from the existing SAS, SSIS, SQL data eco-system.
• Worked on converting existing SAS DI jobs to Azure as part of CRM Phase1 development.Used azure databricks and pyspark for implementing different transformation like JOINS,Lookups at Curation stage and then loaded the data to Azure SQL DW.

• Customer Segmentation Used the customer’s historical data to build cohorts based on similar attributes and characteristics to help the bank drive Customer Strategy and specific targeting with new products.
• Responsibilities Identify the important customer-based data such as demographic, transactional data Clean and merge the dataset to roll up upon customer level
• Create new cross features to capture more customer behaviour such as spending patterns, product portfolio, preferred merchants etc
• Applied K-means clustering algorithm to create customer cohorts ,Labelled each cohort based on the characteristics of each group to identify the relevant cross selling and up-selling opportunities
• Role : Senior Data Analyst Technology: Python,SQL,Excel,Tableau,Qlikview Portfolio Insights and Reporting Credit Cards In order to effectively produce quality decisions in the modern credit card industry, knowledge must be gained through effective data analysis and modelling. Using dynamic data-driven decision-making tools and procedures, information can be gathered to successfully evaluate all aspects of credit card operations. Specifically, areas of bankruptcy, fraud, and collections were focused upon to show the salutary benefits implementation of such practices could provide.
• Responsibilities Prepared data mart for origination applications for credit cards and compare the application trend analysis in terms of received, approved, booked, cancelled and declined applications.
• Prepared account performance management from active vs new origination,first time defaulter vs non-defaulter, delinquent account bucketing, MOB analysis and performance tracking based on current balance to utilised balance to OTB.
• Prepare Charged-Off and Write-Off roll rate analysis and prepare report on annualised loss, rate analysis.
• Analysed the Canvas market and create meaningful customer segments through Cluster Analysis with recommendations that will enable the business to target profitable customers
• Prepared KPI on Origination based on Total Application received vs approved vs booked vs cancelled vs declined | Product wise demand | Average limit per account | Average beacon score for application received | Exception based approvals |New Limit vs old limit average limit per account by each customer category| Region vs Province Vs Branch wise Target
• Role : Data Engineer Technology : SAS, SQL, SAS/BASE, SAS/MACRO, SAS/SQL, SAS DI,SAS/ADVANCE, Excel Summary: As part of Platform upgrade and continuous improvement there were four major enhancement releases to CRM program.
• SMS project where migrated from existing SAS platform to new version of SAS tools
• Data Cleansing to remove inactive customer for UK market based on GDPR rules
• Supplier segmentation based on risk management, sales & marketing, supply management.
• Created ETLs to integrate different source system to store data at different stageing layers of SMS and finally sending the data to campaign team.
• Responsibilities Created SAS DI jobs to build ETL’s to integrate CRM huge data to SQL DB. Extensively used SAS DI transforms to Efficiently integrate and transform raw data (Eg Csv,DB,Excel etc) as per reporting needs for business. Optimized ETLs to load data to SQL DB using right index and table loader techniques.
• Create technical Design document with design to integrate calculations for all the markets across different layers for DI studio LA,DDS,TRF,CMDM,OA.
• Created Base SAS script as part of UK data cleanse to remove inactive customer data from different SMS layers.
• Created SPK packages using SAS management console and migrated code from one environment to other. Worked with different team to set the environment -Dev, UAT,Pre-Prod,Prod and Cutover instruction and handover documents for production support team.