Bajaj Finserv
Website:
bajajfinserv.in
Job details:
Location Name: Pune Corporate Office - Weikfield
Job Purpose
To architect, govern, and scale enterprise-grade Real-Time Approval and Analytics-driven Decisioning platforms that enable high-velocity, frictionless customer acquisition across products. This role is accountable for defining the technical vision, platform architecture, and operating standards for standardized approval engines while leading multiple data science and engineering squads. By driving adoption of advanced machine learning, real-time data processing, and automation frameworks, the role ensures consistent performance, reliability, and scalability of approval ecosystems. The primary objective is to institutionalize data-powered decision platforms that support rapid business expansion, operational excellence, and achievement of LRS financial growth 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 BRD 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 realization.| Coordinate 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 or rejection of data science use cases based on data availability and quality, model feasibility, platform and infrastructure readiness, development effort, and overall cost–value assessment.
- Prioritization of data science initiatives (model development, advanced analytics, automation, AI/ML deployments) aligned with business impact, leadership priorities, and delivery capacity.
- Approval and governance of model go-live plans, including validation, compliance, performance benchmarks, and coordination of final sign-offs with Business and 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
Post Graduate degree in Data Science, Artificial Intelligence, Machine Learning, Computer Science, Statistics, Engineering, or related quantitative disciplines.
Executive certifications in AI/ML Leadership, Cloud Architecture, Product Strategy, or Advanced Analytics will be an added advantage.
- Work Experience
- 8-12 years of experience across data science, advanced analytics, ML engineering, and large-scale digital platform development, preferably in BFSI or high-volume transactional ecosystems.
- Proven experience leading end-to-end analytics and real-time decisioning platforms supporting mission-critical business functions such as loan approvals, customer onboarding, and acquisition funnels.
- Strong expertise in solution architecture design including real-time data pipelines, feature store ecosystems, model orchestration layers, and low-latency inference platforms.
- Hands-on understanding of:
o Production-grade ML systems and MLOps frameworks
o Distributed data processing platforms (Spark, streaming systems, real-time event processing)
o Cloud-native architectures and scalable API-driven systems
o Enterprise data governance, security, and compliance standards
- Demonstrated ability to build, mentor, and scale high-performing data science teams, including talent development, technical coaching, and performance management.
- Strong experience partnering with senior business leaders, product heads, and technology leadership to shape analytics strategy and roadmap priorities.
- Expertise in defining platform-level KPIs, operational SLAs, business impact metrics, and performance governance frameworks.
- Proven capability in managing multi-stream delivery programs, prioritizing initiatives based on ROI, risk, and scalability impact.
- Deep understanding of regulatory, compliance, explainability, and risk management requirements applicable to BFSI AI-driven decision systems.
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