Data Scientist

Neostats Analytics Solutions; Doha; On-site; QAR 200,000 - 400,000; Full time

Job summary

Neostats Analytics Solutions is seeking a senior data scientist to drive predictive modelling and advanced analytics for credit card portfolios in a banking context. You will build segmentation, campaign analytics, and portfolio insights, translate findings into business actions, and collaborate with marketing, product, risk and technology teams.

5+ years in data science or analytics with Python, SAS and SQL, plus experience in campaign analytics, model validation and data visualization tools

Qualifications

• Minimum 5+ years of experience in Data Science or Advanced Analytics in banking.
• Hands-on expertise in Python, SAS and SQL.
• Experience with predictive modelling and machine learning techniques.
• Experience with large banking datasets including card transactions and campaigns.
• Ability to translate business requirements into analytical use cases.

Responsibilities

• Develop predictive models to identify customer targeting opportunities and reduce churn.
• Develop customer segmentation strategies for card portfolios.
• Analyze marketing campaign performance and develop targeting strategies.
• Develop analytical dashboards and management presentations.
• Translate business objectives into analytical use cases and data science solutions.
• Collaborate with marketing, product, risk, and tech teams to implement analytics solutions.

Skills

Python

SAS

SQL

Predictive modelling

Statistical analysis

Machine learning

Tableau

Power BI

Stakeholder management

Card analytics

Tools

Tableau

Power BI

Job description

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.