Joy Kutty Daniel

Successfully completed 18 plus remediation verifications with NIL audit findings.

Lead Analytical Strategy for Remediation Programs

• Independently define the analytical framework for identifying impacted customers, calculating harm, and determining remediation amounts.
• Translate regulatory expectations and business requirements into measurable analytical approaches.

Build and Validate Remediation models

• Develop complex logic, segmentation, and algorithms to quantify customer impact.
• Perform scenario analysis, sensitivity testing, and model validation to ensure accuracy and fairness.
• Validate Business requirements, operational errors, data sources or data logic to identify customer harm.
• Identify flaws in remediation method , data logic and raise findings for financial or non financial impact..

Data Quality and governance

• Ensure data integrity across multiple systems and data sources.
• Establish controls, documentation, and audit‑ready analytical artifacts.
• Collaborate with data and governance teams to enhance data availability and reliability.

Continuous Improvement and Innovation

• Identify opportunities to minimize remediation workflows and analytical processes.
• Suggest improvements in customer fairness, operational efficiency, and risk reduction.

• Data Management– Periodic data ingestion from more than 20 sources, ensuring data quality, data integrity, subject oriented data models, identifying new data sources, data governance, database management and backup. Manages 30TB in SQL Server and 6TB in Cloudera specific to data science projects and ensures single source of truth. (Hadoop environment, RDBMS)
• Data science - Build analytical models based on business needs (statistical, rule based, neural etc.). More than 30 live predictive models covering Micro Segmentation, Personas, Propensity to buy, Retention, Loan and Card Pricing, Customer Journey, Card portfolio models, Lifetime value, and Loyalty and Customer engagements. Models periodically assessed for relevance, bias and effectiveness, and seek business approval of model responses and impact to business. (SAS, Python )
• Business Portfolio and Performance –Provide deep business insights to develop portfolio strategy and responsible for revenue generation. Design campaigns with target segment, offers and channels, does A-B testing and track outcomes. Implement Life cycle of segment/ product track performance of portfolio campaigns. Generated USD 60M revenue in 2019 which is 40% of total new revenue in 2019. (SAS VA, Tableau, SSRS reports)
• Campaign Operation- Execute campaigns designed by Portfolio team as well and generic campaigns designed by product/ segment. Team ensures customer selection, target channels, marketing collateral and offer as per approved program. (IBM Campaign Automation). More than two thousand campaigns executed in 2019.
• Business Performance Management- Conceptualize, design, and operationalized frontline Sales variable schemes. Performance monitoring of various teams and suggest directional change to schemes for optimum productivity and profitability. Develop dashboards to channels for their portfolio and performance.
• Key Responsibilities
• Advisory to $1B revenue business to grow and retain Customer, Revenue & Balance Sheet through Customer awareness, engagement, and strategic direction.
• Building agile cross functional team to ensure timely and efficient customer engagement with decision science, optimized channel, and communication collateral.
• Define and refine Customer Strategy in Segmentation, Proposition, Customer Engagement and Experience, Sales and Service model, Pricing Structures, New segment, or Product.
• Ensure Channel performance for productivity, cost, and revenue in line with targets. Suggest ways to improve outcomes or change direction.
• Develop Life Cycle Management across micro segments. Ensure revenue management by product penetration (X-sell), Upsell and Retention management. Provide business direction on periodic and ad-hoc basis for each line of business on portfolio health and suggest ways forward.
• Ensure collaboration with key stakeholders: business, Marketing Communication, IT, Finance, HR, Information Security and System Audit for seamless operation.
• Evangelization of data sciences throughout business by highlighting various use case scenarios of advanced analytics.
• Ensure infrastructure in place and review performance of tools & technology. Regular upgrades and change to tools undertaken for optimal performance.