Data Scientist & Analytics Lead- Customer Excellence

Unity Small Finance Bank; Navi Mumbai; On-site; INR 1,800,000 - 2,500,000; Full time

Job summary

Unity Small Finance Bank seeks a seasoned Data Scientist to lead propensity modelling, lead scoring, and the design of a personalized recommendation engine for digital banking products. You will develop end-to-end models, validate them rigorously, and deploy them into production while collaborating with Product, IT, and CRM teams to translate analytics into measurable business impact.

The role requires 5–10 years of experience in data science within BFSI, strong statistical and modeling skills,

Qualifications

• B.Tech/M.Tech/MSc in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative discipline from a reputed institution.
• Certifications in ML/AI (AWS ML Specialty, Google Professional ML Engineer, Databricks, etc.) will be an advantage.
• Certifications in Data Science / ML / Cloud (AWS / Azure / GCP) are advantageous.
• 5 - 10 years of progressive experience in data science, machine learning, and analytics in BFSI (banking, NBFC, fintech, insurance).
• Demonstrated track record of building and deploying propensity or scoring models in a banking or NBFC environment.
• Prior experience implementing or contributing to a recommendation engine in a digital banking or fintech context will be highly preferred.

Responsibilities

• Design and develop end-to-end propensity models and lead scoring engines for banking products.
• Build and validate binary and multi-class classification models to predict purchase, activation, or churn.
• Deploy models into production pipelines and integrate outputs with CRM, campaign, and operations systems.
• Architect a dynamic lead scoring framework and integrate with CRM for real-time decisioning.
• Design a hyper-personalized recommendation engine across mobile apps, net banking, IVR, and agent workflows.
• Collaborate with Sales, Operations, and Technology teams to embed scored leads into automated processes.

Skills

Propensity modelling

Lead scoring

Recommendation engine

ML / Data science

Data engineering

Banking domain

Education

B.Tech / M.Tech / M.Sc in CS/Statistics/Math/Data Science

Tools

AWS ML Specialty

Google Professional ML Engineer

Databricks

Job description

Role & responsibilities

A. Propensity Modelling & Predictive Analytics

• Design and develop end-to-end propensity models based recommendation engine and/ or lead scoring engine for products including savings accounts, FDs, personal loans, microfinance, insurance, and gold loans.
• Build binary and multi-class classification models to predict customer likelihood to purchase, activate, or churn.
• Conduct feature engineering using customer demographics, transaction behaviour, bureau data, and digital footprint.
• Regularly validate, recalibrate, and champion-challenger test models to maintain accuracy and lift.
• Deploy models into production pipelines and integrate outputs with CRM, campaign, and operations systems.

B. Lead Scoring Engine

• Architect and implement a dynamic lead scoring framework that ranks prospects and existing customers by conversion probability.
• Integrate lead scoring outputs with the bank's CRM and other core systems for real-time decisioning.
• Define scoring thresholds, segment-wise cut-offs, and feedback loops based on sales team performance data.
• Develop dashboards and reports to track lead funnel performance, score distribution, and model ROI.
• Collaborate with the Sales, Operations, and Technology teams to embed scored leads into automated workflows.

C. Recommendation Engine (Usage & Implementation)

• Conceptualise, design, and implement a hyper-personalized recommendation engine for product suggestions across digital channels (mobile app, net banking, IVR, agent assist).
• Evaluate and recommend appropriate algorithmic approaches collaborative filtering, content-based filtering, hybrid models, or deep learning-based techniques aligned to data availability and business objectives.
• Drive end-to-end implementation of the recommendation engine in collaboration with IT, Digital, and Product teams.
• Define usage guidelines, guardrails, and governance policies for responsible deployment of recommendations.
• Measure recommendation engine performance via CTR, conversion lift, next-best-action acceptance rates, and revenue attribution.
• Continuously improve recommendation relevance by incorporating real-time behavioural signals, seasonality, and campaign feedback.

D. ML based models Workflow Automation

• Develop analytical models to support operational efficiency queue prediction, TAT optimisation, fraud propensity, early warning signals, and NPA prediction.
• Partner with cross-functional teams to identify automation opportunities and build supporting data models.
• Build and maintain data pipelines, feature stores, and model monitoring frameworks.
• Generate insights from structured and unstructured data (call centre logs, app events, branch transactions) to inform process automation priorities.

E. Stakeholder Collaboration & Business Translation

• Work closely with Product, CRM, and business teams to define analytical requirements and deliver actionable insights.
• Present model outputs, recommendations, and business impact to senior management in a clear, non-technical manner.
• Create and manage model documentation, business sign-off artefacts, and model governance records.

Preferred candidate profile

Core Data Science & Machine Learning

Data Engineering & Platforms

Banking Domain Knowledge

• 1. B.Tech / M.Tech / M.Sc in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative discipline from a reputed institution.
• 2. Certifications in ML / AI (AWS ML Specialty, Google Professional ML Engineer, Databricks, etc.) will be an advantage.
• 3. Certifications in Data Science / ML / Cloud (AWS / Azure / GCP) are advantageous.
• 4. 5 - 10 years of progressive experience in data science, machine learning, and analytics in BFSI (banking, NBFC, fintech, insurance).
• Demonstrated track record of building and deploying propensity or scoring models in a banking or NBFC environment.
• 5. Prior experience implementing or contributing to a recommendation engine in a digital banking or fintech context will be highly preferred.