Bajaj Finserv
Website:
bajajfinserv.in
Job details:
Location Name: Pune Corporate Office - Weikfield
Job Purpose
To design, build, and operationalize a scalable Real-Time Approval and Decisioning ecosystem powered by advanced analytics and machine learning, enabling organizational agility at scale. This role is responsible for developing a standardized, product-specific approval engine that ensures ultra-fast, data-driven customer onboarding and acquisition. By leveraging real-time data pipelines, predictive models, and automation frameworks, the role aims to eliminate friction across the customer journey, optimize funnel performance, and enable rapid business scaling aligned with LRS financial growth objectives.
Duties And Responsibilities
Data Analytical Solution Development
- As a strategic interface between Business/COEs, Data Science, and Technology teams to translate business objectives into scalable real-time approval, analytics, and ML-driven decisioning solutions.
- Evaluate business requirements and user stories for assigned streams, perform gap analysis, and co-create solution architecture, approach documents, and data flow designs in collaboration with engineering teams.
- Break down complex business problems into structured analytical requirements, feature definitions, model inputs, and measurable success metrics.
- Enhance solution by incorporating customer experience, data-driven decisioning logic, standardization principles, and horizontal scalability considerations.
- Review technical and data solutioning documents to identify gaps related to model integration, real-time inference performance, data latency, and business logic alignment.
- Ensure complete functional and analytical coverage across upstream and downstream system integrations supporting the Real-Time Approval Engine.
- Define data ecosystem and feature store requirements, including real-time ingestion, transformation, and serving layers required for approval workflows and ML pipelines.
- Proactively identify product enhancement opportunities using funnel analytics, customer behavior insights, and performance trends, and evaluate feasibility with technology teams.
- Track emerging analytics platforms, ML frameworks, and real-time processing technologies to drive innovation and continuous product modernization.
- Participate in product roadmap reviews, feature prioritization discussions, and quarterly planning sessions with business leadership to align analytics-led capabilities with growth objectives.
- Build and manage execution roadmaps for analytics and decisioning initiatives, prioritizing high-impact and cross-business use cases.
- Define critical success factors, business KPIs, model performance metrics (accuracy, latency, stability), and operational SLAs prior to launch.
- Post-release, continuously monitor product health, model performance, system uptime, and business outcome metrics to ensure consistent SLA adherence.
Project Management and control
- Own end-to-end requirement intake for analytics, data, and integration components from COEs to initiate real-time approval and automation projects.
- Perform benchmarking and competitive analysis to identify best-in-class decisioning and approval practices across BFSI and digital lending ecosystems.
- Maintain centralized delivery trackers and dashboards for leadership reviews covering milestones, risks, dependencies, and delivery status.|? Coordinate closely with engineering and data platform teams on roadmap planning, release cycles, testing schedules, and production rollouts.
- Actively participate in UAT validation of Real-Time Approval Engine use cases, including rule logic, model outputs, and end-to-end customer journey flows.
- Review new business use cases to assess data readiness, system compatibility, model feasibility, and incremental development effort required.
- Ensure solution designs remain scalable, modular, and performance-optimized while simplifying operational complexity.
Post Product release maintenance and support
- Continuously review application, model, and business performance post-deployment to ensure stability, accuracy, and operational excellence.
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- Monitor real-time data movement, feature freshness, and pipeline SLAs across ingestion, processing, and inference layers.
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- Conduct impact analysis for production changes, defect fixes, model upgrades, and enhancement releases.
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- Track technical KPIs, funnel conversion metrics, approval turnaround time (TAT), drop-off rates, and pre/post approval journey performance.
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- Design and analyze pre-launch pilots, A/B experiments, and post-launch performance reports to measure customer response and business uplift.
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- Drive continuous improvement cycles by refining models, rules engines, and decision logic based on observed performance trends and business feedback.
Key Decisions / Dimensions
Following Decisions Are Taken By The Role
- Acceptance and Rejection of Business use cases based on system capabilities and effort and cost required to develop the product feature.
- 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
- Qualifications
- Post Graduate/Graduate degree in Engineering, Data Science, Statistics, Computer Science, or related quantitative disciplines.
- Work Experience
- 2-4 years of experience across analytics, machine learning, product enablement, or digital business transformation roles.
- Strong understanding of real-time data processing systems, loan management systems (LMS), financial applications, and consumer-facing digital platforms.
- Hands-on experience with:
- ML model development and deployment for decisioning use cases
- Funnel analytics, campaign optimization, and customer journey analysis
- Automation frameworks and scalable data pipelines
- Working knowledge of model governance, performance monitoring, and explainability frameworks.
- Experience in pre-product research, solution prototyping, and rapid experimentation is an added advantage.
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