Principal Architect, Advanced Analytics & CLTV Solutions (Telecom)

Job description

Role : Solution Architect - Machine Learning (Telecom Churn & Advanced Analytics)

Experience Level : 8 to 12 Years

Location : India - Bangalore / Mumbai

Vision:

In the highly competitive and dynamic telecommunications industry, retaining customers and maximizing their lifetime value are paramount.

We are building a specialized team focused on designing and implementing cutting-edge analytical solutions, particularly in churn modeling, intervention strategies, and customer lifetime value (CLTV) analysis.

Our goal is to empower telecom companies with actionable insights to proactively manage customer relationships, reduce churn, and optimize revenue across both consumer and enterprise segments.

This team will leverage the latest tools and techniques, including classical and deep learning models, and the emerging power of LLMs, to deliver production-grade, enterprise-scale solutions.

Overview:

We are seeking a highly experienced Solution Architect with 8-10 years of experience, specializing in churn modeling and advanced analytics within the telecommunications domain.

You will be responsible for architecting end-to-end analytical solutions, from data ingestion and sophisticated model development to deployment, monitoring, and continuous improvement, specifically for telecom companies.

This role requires deep expertise in churn prediction, intervention models, survival analysis, and CLTV analysis, coupled with a strong understanding of the telecom industry's unique challenges, including network performance and latency.

You will lead the technical design, guide implementation teams, and engage directly with clients to translate complex business problems into robust, scalable, and explainable analytical solutions for both consumer and enterprise customers.

The position involves working with a diverse, lively, and proactive group who are constantly raising the bar on translating the latest AI research into tangible reusable assets for the community.

Hence this would require a high level of conceptual understanding, attention to detail and agility in terms of adaptation to new technologies.

Roles and Responsibilities:

• As a core member of the Solution Architecture team, you will provide technical leadership and strategic direction for analytical projects, particularly in churn modeling and customer analytics for telecom clients.
• Architect production-grade enterprise-level churn modeling solutions, leveraging a diverse range of techniques including classical ML (Random Forest, Bagging & Boosting techniques, Ensemble models) and Deep Learning models.
• Explore and integrate the usage of Large Language Models (LLMs) in predictive analytics for churn, potentially for feature engineering, anomaly detection, or understanding customer feedback.
• Design and implement intervention models to proactively address identified churn risks, considering various customer segments and service types.
• Lead the application of Survival Analysis techniques to understand customer tenure and predict churn events more accurately.
• Develop comprehensive Customer Lifetime Value (CLTV) analysis frameworks to identify high-value customers and optimize retention strategies.
• Conduct in-depth data analysis, including understanding data distribution, performing feature importance analysis, and applying advanced feature selection techniques.
• Implement and champion model explainability techniques (e.G., SHAP, LIME) to provide transparent insights into model predictions.
• Design and implement robust drift detection techniques for both data and model drift, ensuring the long-term accuracy and relevance of deployed models.
• Evaluate and recommend the latest tools, technologies, and methodologies for churn prediction, customer analytics, and related use cases within the telecom domain.
• Translate complex business requirements from telecom clients (both consumer and enterprise) into detailed technical specifications and architectural designs.
• Provide technical guidance and mentorship to data scientists, ML engineers, and development teams throughout the project lifecycle.
• Ensure the scalability, reliability, performance, and security of analytical solutions in a production environment.
• Engage directly with clients, presenting complex analytical concepts and solutions in a clear and compelling manner to both technical and non-technical stakeholders.
• Stay abreast of industry trends, emerging technologies, and best practices in telecom analytics, network performance, and customer behavior.

Skill Set Needed:

• Experience for 8-10 years in Solution Architecture, with a significant focus on advanced analytics and machine learning.
• Proven experience in designing and implementing production-grade enterprise-level churn modeling solutions for telecom companies.
• Deep expertise in developing and deploying intervention models to mitigate churn.
• Strong hands-on experience with Survival Analysis techniques for customer behavior prediction.
• Extensive experience with Customer Lifetime Value (CLTV) analysis and its application in business strategy.
• Proficient in applying a wide range of machine learning techniques, including:
• Classical ML: Random Forest, Bagging & Boosting techniques (e.G., XGBoost, LightGBM), Ensemble models.
• Deep Learning models for predictive analytics.
• Experience with the usage of Large Language Models (LLMs) in predictive analytics is highly desirable.
• Very strong analytical skills with proven experience in:
• In-depth analysis of data and understanding data distribution.
• Feature importance and advanced feature selection techniques.
• Model explainability techniques (e.G., SHAP, LIME).
• Drift detection techniques (data drift, model drift).
• Demonstrated ability to evaluate and apply the latest tools and techniques for churn prediction and customer analytics.
• Strong domain skills in the Telecom industry, including understanding of network infrastructure, latency considerations, and service delivery models.
• Experience with enterprise-level use cases for both consumer and enterprise customers within the telecom sector.
• Proficiency in at least one major cloud platform (e.G., GCP, AWS, Azure) and experience with deploying analytical solutions in a cloud environment.
• Strong understanding of data warehousing, data lakes, and big data technologies relevant to large-scale analytical solutions.
• Excellent communication, presentation, and interpersonal skills, with the ability to interact effectively with senior stakeholders and technical teams.
• Ability to lead technical discussions, make architectural decisions, and guide implementation teams.
• Strong problem-solving and analytical skills, with a strategic mindset.

Good to Have:

• Experience with specific telecom data models and industry standards.
• Familiarity with network performance monitoring and optimization.
• Experience with real-time analytics and streaming data processing.
• Knowledge of regulatory compliance in the telecom industry.
• Certifications in relevant cloud platforms or data science/ML.
• Contributions to thought leadership in telecom analytics or churn management.

Required Skill Profession

Computer Occupations