• To drive data-driven decision-making and business growth by developing advanced analytical models, insights, and customer lifecycle strategies.
• The role is responsible for leveraging statistical techniques, machine learning, and deep customer behavior analysis to optimize CLM initiatives, improve retention and acquisition performance, and enhance overall customer value.
• This position works closely with cross-functional teams to translate complex data into actionable recommendations and support strategic business outcomes.
• In addition, the position involves working with server-side data processing techniques—including SQL optimization, big data platforms to ensure efficient, reliable, and high-performance analytics delivery.
• Analyze structured and unstructured data to identify customer trends, behavioral patterns, lifecycle movements, and revenue opportunities.
• Apply statistical tests, optimization techniques, and hypothesis testing to validate insights and support data-driven business decisions.
• Build and deploy machine learning solutions for CLM use cases such as segmentation, churn prediction, upsell/cross-sell, lifetime value modeling, recommendation systems, and propensity scoring.
• Design, develop, and maintain ETL/ELT pipelines to collect, process, and transform customer and transactional data from multiple sources.
• Build and optimize data warehouses, data lakes, and analytical data models to support CLM dashboards, campaign orchestration, and real-time decisioning.
• Ensure data quality, consistency, availability, and security across all customer data systems and analytical platforms.
• Collaborate with CLM, marketing, product, and sales teams to translate business requirements into analytical solutions and actionable strategies.
• Monitor, troubleshoot, and optimize data pipelines, model accuracy, and system performance to maintain high operational reliability.
• Develop and maintain documentation for data processes, analytical workflows, machine learning models, and system architecture.
• Stay up to date with emerging advanced analytics techniques, cloud platforms, and big data technologies to drive innovation in CLM programs.
• Design end-to-end customer journey analytics to identify lifecycle gaps, trigger points, and personalized engagement opportunities.
• Evaluate campaign performance, improve the uplift to continuously refine CLM strategies and improve customer value.
• Build dashboards and KPI frameworks to track customer retention, churn details, usage trends, and revenue movement.
• Work with IT and related cross-function teams to ensure alignment between analytical models, system rules, and campaign execution platforms.

Education:

• Bachelor’s or master’s degree in data science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
• Advanced degrees (Master’s/PhD) in analytics, machine learning, or applied statistics are an advantage.
• Professional certifications in Machine Learning, Data Engineering, Cloud Platforms (Linux, AWS, Azure) or Big Data technologies are preferred.

Experience:

• 1-3 years of hands-on experience in data science, advanced analytics, or machine learning roles.
• Solid experience working with customer lifecycle management (CLM), CRM analytics, retention models, or customer segmentation in telecom, digital platforms, or similar industries.
• Hands-on experience with SQL, data modeling, and managing large datasets in relational or big data environments.
• Previous involvement in campaign analytics, CLM journey design is a strong advantage.
• Experience collaborating with cross-functional teams (Marketing, CLM, Product, IT, Data Engineering) to deliver analytical solutions.
• End to end data modeling and server technologies
• Practical skills with programming languages such as Python, R, Bash script for analytics and model development.

Skills

• Strong proficiency in SQL and experience with relational and non-relational databases (e.g., MySQL, PostgreSQL, MongoDB, ClickHouse).
• Knowledge of programming languages such as Python, Bash Script
• Experience with big data technologies (e.g., Vertica, Oracle).
• Familiarity with cloud platforms (AWS, Linux platform) and data services (e.g., BigQuery, Snowflake).
• Familiarity with Visualization Tools (PowerBI, Tablaeu)
• Understanding data modeling, warehousing, and data architecture principles.
• Experience with version control (Git) and CI/CD pipelines is a plus.