Data Scientist

Neostats Analytics Solutions; Bengaluru; In loco; INR 2.500.000 - 4.000.000; Tempo pieno

Descrizione del lavoro

Neostats Analytics Solutions is seeking an experienced Data Scientist for its Bengaluru-based analytics practice. You will lead predictive modelling, customer segmentation and marketing analytics for banking portfolios, using Python, SAS and SQL to uncover revenue opportunities and optimize campaigns.

You will collaborate with marketing, product, risk and technology teams to translate data insights into business actions, develop dashboards, and influence portfolio profitability while maintaining

Competenze

• Minimum 5+ years of experience in Data Science, Advanced Analytics or Analytics Consulting.
• Strong banking experience, particularly in credit/debit card analytics.
• Hands-on expertise in Python, SAS and SQL.
• Experience with predictive modelling, statistical analysis and machine learning techniques.

Mansioni

• Develop predictive models to identify customer targeting opportunities and increase card spending.
• Build and optimize propensity, response, churn, cross-sell and upsell models.
• Apply statistical modelling and ML techniques to identify customer behaviour and spending patterns.
• Translate model outputs into actionable business recommendations.
• Develop customer segmentation strategies for card portfolios and analyse transaction patterns.
• Analyze marketing campaign performance and develop data-driven targeting strategies.
• Create analytical dashboards and reports for senior management.

Conoscenze

Python

SAS

SQL

Data analysis

Stakeholder management

Strumenti

Tableau

Power BI

Descrizione del lavoro

Role & responsibilities

1. Predictive Modelling & Advanced Analytics

• Develop predictive models to identify customer targeting opportunities, increase card spending, improve retention and reduce customer churn.
• Build and optimize propensity, response, churn, cross-sell and upsell models.
• Apply statistical modelling and machine learning techniques to identify customer behaviour and spending patterns.
• Perform model validation, performance monitoring and optimization.
• Translate model outputs into actionable business recommendations.

2. Customer Segmentation & Portfolio Analytics

• Develop customer segmentation strategies for credit and debit card portfolios.
• Identify high-value, low-engagement and high-potential customer segments.
• Analyze customer spending behaviour, transaction patterns and product usage.
• Identify opportunities to improve card activation, utilization, spend growth and customer profitability.
• Support customer lifecycle management strategies across acquisition, activation, engagement, retention and attrition.

3. Cards Marketing & Campaign Analytics

• Analyze marketing campaign performance across customer segments, products, channels and transaction behaviour.
• Develop data-driven customer targeting strategies for card marketing campaigns.
• Measure campaign effectiveness, conversion rates, incremental spend and return on investment (ROI).
• Design test-and-learn frameworks, including A/B testing, control groups and Champion/Challenger strategies.
• Provide recommendations to improve campaign effectiveness, customer response and overall portfolio performance.
• Support personalized marketing, offer optimization and next-best-action strategies.

4. Business Intelligence & Data Analysis

• Analyze large banking datasets, including customer profiles, card transactions, spending behaviour and campaign history.
• Develop analytical dashboards, reports and management presentations.
• Identify business trends, portfolio opportunities and potential revenue growth areas.
• Monitor key performance indicators and provide insights to senior management.
• Present complex analytical findings in a clear and commercially meaningful manner.

5. Business Strategy & Stakeholder Management

• Work closely with marketing, product, cards business, risk and technology teams to understand business requirements.
• Translate business objectives into analytical use cases and data science solutions.
• Provide analytical recommendations to improve customer engagement, card spending and portfolio profitability.
• Collaborate with business stakeholders to implement data-driven marketing and portfolio strategies.
• Ensure analytical solutions are practical, explainable and aligned with business objectives.

6. Business Knowledge Mandatory

• Strong understanding of the credit and debit card business, including card acquisition, issuance, activation, usage, spend growth, retention and attrition.
• Knowledge of card revenue drivers, including interchange income, interest income, annual fees, customer profitability and transaction economics.
• Understanding of customer lifecycle management, portfolio growth strategies, cross-selling, upselling and customer engagement.
• Knowledge of card marketing campaigns, customer targeting, offer optimization, promotional strategies and campaign ROI.
• Ability to identify business opportunities through customer behaviour, spending patterns, transaction trends and portfolio performance.
• Understanding of card portfolio KPIs, including active cards, spend per card, transaction volume, activation rates, utilization, attrition and customer lifetime value.
• Knowledge of banking products, customer segmentation, credit risk, portfolio profitability and risk-adjusted growth strategies.
• Ability to translate business requirements into analytical solutions and actionable recommendations that drive revenue growth and improve customer engagement.

Preferred candidate profile

• Minimum 5+ years of experience in Data Science, Advanced Analytics or Analytics Consulting.
• Strong banking experience, particularly in credit card and debit card analytics.
• Hands-on expertise in Python, SAS and SQL.
• Strong experience in predictive modelling, statistical analysis and machine learning techniques.
• Experience working with large banking datasets, including customer profiles, card transactions and campaign history.
• Proficiency in customer segmentation, campaign analytics and portfolio analytics.
• Understanding of model validation, performance tracking and optimization.
• Experience with data visualization tools such as Tableau, Power BI or similar platforms.
• Ability to develop analytical reports, dashboards and senior-level presentations.
• Experience working with marketing, product, risk and data teams in a banking environment.
• Strong analytical, problem-solving and stakeholder-management capabilities.
• Experience in card lifecycle analytics, including acquisition, activation, usage, spend growth, retention and attrition.
• Familiarity with campaign management platforms, CRM systems or marketing automation tools.
• Experience with A/B testing, uplift modelling, next-best-action, offer optimization or customer lifetime value models.
• Knowledge of cloud-based data platforms and modern analytics environments.
• Experience working directly with banking clients in a consulting or client-facing environment.