Cluster Manager Customer Centricity UnitSenior Cluster Manager Customer Centricity Unit

Pune - India

Monthly Salary: Not Disclosed

Posted on: 10 hours ago

Vacancies: 1 Vacancy

Job Summary

Job Purpose	To build and scale advanced Real-Time Approval and Analytics-driven Decisioning systems that enable high-speed data-powered customer acquisition at enterprise scale. This role is responsible for leading the design and deployment of standardized product-specific approval engines using machine learning real-time data pipelines and automation frameworks. Acting as a technical owner the role ensures low-latency high-accuracy decisioning while continuously optimizing customer journeys funnel performance and approval efficiency. The primary objective is to institutionalize robust scalable and production-ready data science solutions that eliminate operational friction and accelerate business growth aligned with LRS financial objectives.
Duties and Responsibilities	Data Analytical Solution Development Act as the primary analytics and decisioning lead interfacing between Business/COEs Product Data Engineering and Technology teams to translate business vision into scalable Real-Time Approval and ML-driven decisioning solutions. Own end-to-end solution design for assigned product streams by reviewing business user stories conducting gap analysis and driving creation of architecture blueprints analytical approach documents and data flow designs in collaboration with engineering teams. Lead decomposition of complex business and regulatory requirements into structured analytical workflows model specifications feature engineering requirements and measurable business outcomes. Govern enrichment by embedding customer-centric design standardization principles reusability frameworks and horizontal scalability across product lines. Review and approve technical and data solutioning documents to ensure alignment with approved BRDs model governance standards latency SLAs and production-readiness criteria. Drive end-to-end integration coverage across upstream data sources real-time processing layers model inference services and downstream business systems. Define and oversee data ecosystem architecture including feature store strategy real-time ingestion pipelines model deployment patterns and monitoring frameworks supporting the RT Approval Engine. Mentor junior data scientists and analysts on solution design modeling best practices and business context to build strong analytical capability within the team. Proactively identify platform enhancement opportunities using performance analytics funnel insights and experimentation results and prioritize initiatives based on business impact. Track emerging technologies in real-time analytics ML platforms and decisioning engines and recommend adoption strategies aligned with organizational roadmap. Represent the analytics and data science charter in product governance forums roadmap discussions quarterly planning sessions and leadership reviews. Own definition of critical success metrics including business KPIs model accuracy latency SLAs stability thresholds and automation efficiency prior to production rollout. Establish continuous performance monitoring frameworks post-launch to ensure sustained SLA compliance model reliability and business outcome realization. Project Management and control Lead intake prioritization and execution planning of analytics ML and integration requirements from COEs across multiple concurrent initiatives. Conduct competitive benchmarking and industry trend analysis to guide roadmap decisions and product evolution. Own consolidated delivery trackers and executive dashboards for leadership reporting on milestones risks dependencies and value cross-functional delivery squads including engineering QA platform and business stakeholders to ensure on-time and high-quality releases. Lead UAT validation efforts for Real-Time Approval Engine use cases including rule logic validation model performance testing and end-to-end journey verification. Review and approve business use cases from feasibility scalability and long-term maintainability perspectives. Ensure architectural simplicity modularity and scalability while maintaining enterprise-grade performance and reliability standards. Post Product release maintenance and support Own post-release performance governance across application stability data pipelines ML models and business KPIs. Ensure real-time data movement feature freshness and SLA adherence across ingestion transformation and inference layers. Lead root cause analysis for production issues performance degradation and model drift driving corrective action plans. Oversee impact assessments for platform changes enhancement releases and model upgrades to minimize business disruption. Monitor end-to-end funnel performance approval turnaround time drop-off rates and pre/post approval experience metrics. Design and supervise experimentation frameworks including A/B testing pilot rollouts and controlled releases to measure business uplift. Drive continuous optimization cycles by refining decision logic retraining models tuning rules engines and improving automation workflows.
Key Decisions / Dimensions	Following decisions are taken by the role: Acceptance and Rejection of Business use cases based on system capabilities and cost required to develop the feature. Project task prioritization based on management and leadership view. Collecting signoffs and Go live plan from Business/COE Heads.
Major Challenges	Changing scope of project by business/COE leads to lot of rework. Upskilling on system compatibilities and capabilities w.r.t Loan approval workflows. Tech debt and legacy systems change management and performance related challenges Clear visibility on data flow from core systems to ancillary applications in Real time.
Required Qualifications and Experience	a)Qualifications Post Graduate/Graduate degree in Data Science Machine Learning Computer Science Statistics Applied Mathematics Engineering or related quantitative disciplines. Professional certifications in Cloud Platforms ML Engineering Data Engineering or Product Analytics will be an added advantage. b)Work Experience 47 years of relevant experience across data science advanced analytics ML-driven product development digital lending platforms or real-time decisioning systems. Strong experience working at the intersection of data science product engineering and business stakeholders to deliver scalable analytics solutions. Deep understanding of real-time data processing systems loan management systems (LMS) financial platforms and BFSI digital ecosystems. Hands-on expertise in: oMachine learning model development validation deployment and monitoring oReal-time inference pipelines and API-based model serving oFeature engineering feature store design and data pipeline optimization oFunnel analytics customer journey analysis and campaign performance optimization Proven ability to lead pre-product research solution prototyping experimentation (A/B testing) and rapid iteration cycles. Experience with cloud-based data platforms distributed computing frameworks and ML lifecycle management tools (MLflow CI/CD pipelines model registries). Strong knowledge of model governance explainability frameworks bias monitoring performance tracking and regulatory compliance requirements in BFSI environments. Demonstrated capability to mentor junior team members review technical deliverables and guide best practices adoption. Strong stakeholder management communication and solution storytelling skills to influence product decisions and leadership alignment. Ability to manage multiple concurrent initiatives balance delivery timelines and drive outcome-oriented execution.

Required Experience:

Manager

Job PurposeTo build and scale advanced Real-Time Approval and Analytics-driven Decisioning systems that enable high-speed data-powered customer acquisition at enterprise scale. This role is responsible for leading the design and deployment of standardized product-specific approval engines using mach...

Job Purpose	To build and scale advanced Real-Time Approval and Analytics-driven Decisioning systems that enable high-speed data-powered customer acquisition at enterprise scale. This role is responsible for leading the design and deployment of standardized product-specific approval engines using machine learning real-time data pipelines and automation frameworks. Acting as a technical owner the role ensures low-latency high-accuracy decisioning while continuously optimizing customer journeys funnel performance and approval efficiency. The primary objective is to institutionalize robust scalable and production-ready data science solutions that eliminate operational friction and accelerate business growth aligned with LRS financial objectives.
Duties and Responsibilities	Data Analytical Solution Development Act as the primary analytics and decisioning lead interfacing between Business/COEs Product Data Engineering and Technology teams to translate business vision into scalable Real-Time Approval and ML-driven decisioning solutions. Own end-to-end solution design for assigned product streams by reviewing business user stories conducting gap analysis and driving creation of architecture blueprints analytical approach documents and data flow designs in collaboration with engineering teams. Lead decomposition of complex business and regulatory requirements into structured analytical workflows model specifications feature engineering requirements and measurable business outcomes. Govern enrichment by embedding customer-centric design standardization principles reusability frameworks and horizontal scalability across product lines. Review and approve technical and data solutioning documents to ensure alignment with approved BRDs model governance standards latency SLAs and production-readiness criteria. Drive end-to-end integration coverage across upstream data sources real-time processing layers model inference services and downstream business systems. Define and oversee data ecosystem architecture including feature store strategy real-time ingestion pipelines model deployment patterns and monitoring frameworks supporting the RT Approval Engine. Mentor junior data scientists and analysts on solution design modeling best practices and business context to build strong analytical capability within the team. Proactively identify platform enhancement opportunities using performance analytics funnel insights and experimentation results and prioritize initiatives based on business impact. Track emerging technologies in real-time analytics ML platforms and decisioning engines and recommend adoption strategies aligned with organizational roadmap. Represent the analytics and data science charter in product governance forums roadmap discussions quarterly planning sessions and leadership reviews. Own definition of critical success metrics including business KPIs model accuracy latency SLAs stability thresholds and automation efficiency prior to production rollout. Establish continuous performance monitoring frameworks post-launch to ensure sustained SLA compliance model reliability and business outcome realization. Project Management and control Lead intake prioritization and execution planning of analytics ML and integration requirements from COEs across multiple concurrent initiatives. Conduct competitive benchmarking and industry trend analysis to guide roadmap decisions and product evolution. Own consolidated delivery trackers and executive dashboards for leadership reporting on milestones risks dependencies and value cross-functional delivery squads including engineering QA platform and business stakeholders to ensure on-time and high-quality releases. Lead UAT validation efforts for Real-Time Approval Engine use cases including rule logic validation model performance testing and end-to-end journey verification. Review and approve business use cases from feasibility scalability and long-term maintainability perspectives. Ensure architectural simplicity modularity and scalability while maintaining enterprise-grade performance and reliability standards. Post Product release maintenance and support Own post-release performance governance across application stability data pipelines ML models and business KPIs. Ensure real-time data movement feature freshness and SLA adherence across ingestion transformation and inference layers. Lead root cause analysis for production issues performance degradation and model drift driving corrective action plans. Oversee impact assessments for platform changes enhancement releases and model upgrades to minimize business disruption. Monitor end-to-end funnel performance approval turnaround time drop-off rates and pre/post approval experience metrics. Design and supervise experimentation frameworks including A/B testing pilot rollouts and controlled releases to measure business uplift. Drive continuous optimization cycles by refining decision logic retraining models tuning rules engines and improving automation workflows.
Key Decisions / Dimensions	Following decisions are taken by the role: Acceptance and Rejection of Business use cases based on system capabilities and cost required to develop the feature. Project task prioritization based on management and leadership view. Collecting signoffs and Go live plan from Business/COE Heads.
Major Challenges	Changing scope of project by business/COE leads to lot of rework. Upskilling on system compatibilities and capabilities w.r.t Loan approval workflows. Tech debt and legacy systems change management and performance related challenges Clear visibility on data flow from core systems to ancillary applications in Real time.
Required Qualifications and Experience	a)Qualifications Post Graduate/Graduate degree in Data Science Machine Learning Computer Science Statistics Applied Mathematics Engineering or related quantitative disciplines. Professional certifications in Cloud Platforms ML Engineering Data Engineering or Product Analytics will be an added advantage. b)Work Experience 47 years of relevant experience across data science advanced analytics ML-driven product development digital lending platforms or real-time decisioning systems. Strong experience working at the intersection of data science product engineering and business stakeholders to deliver scalable analytics solutions. Deep understanding of real-time data processing systems loan management systems (LMS) financial platforms and BFSI digital ecosystems. Hands-on expertise in: oMachine learning model development validation deployment and monitoring oReal-time inference pipelines and API-based model serving oFeature engineering feature store design and data pipeline optimization oFunnel analytics customer journey analysis and campaign performance optimization Proven ability to lead pre-product research solution prototyping experimentation (A/B testing) and rapid iteration cycles. Experience with cloud-based data platforms distributed computing frameworks and ML lifecycle management tools (MLflow CI/CD pipelines model registries). Strong knowledge of model governance explainability frameworks bias monitoring performance tracking and regulatory compliance requirements in BFSI environments. Demonstrated capability to mentor junior team members review technical deliverables and guide best practices adoption. Strong stakeholder management communication and solution storytelling skills to influence product decisions and leadership alignment. Ability to manage multiple concurrent initiatives balance delivery timelines and drive outcome-oriented execution.

Required Experience:

Manager

Key Skills

Nurse Management
Acute Care
Computer Skills
Data Collection
Management Experience
Conflict Management
Dementia Care
Medical Terminology
Nursing
Operations Management
Leadership Experience
Supervising Experience

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About Company

Bajaj Finance Limited

Bajaj Finserv is India’s most diversified non-banking finance company. Visit the website to learn about our products. Get instant approval on loans, shop on EMIs, make an investment, get insurance and pay your bills.

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