Job Description & How to Apply Below
About the role:
As a Sr . Lead Data Analyst at Airtel, you will play a pivotal role in driving data-backed decision-making by providing actionable insights across various business functions. You will work independently to analyze complex business data related to sales, revenue, customer experience, and financial operations (billing, payments, collections). You will also contribute to the development of AI-driven solutions that improve business performance and optimize processes.
In this role, you will take ownership of your deliverables and work directly with cross-functional teams, including Engineering, Marketing, Product, and Customer Experience, to ensure the successful execution of analytics initiatives. You will also be expected to set an example for high-quality execution, share best practices, and mentor junior analysts to build a strong analytics foundation within the team.
Key Responsibilities:
End-to-End Analytics Execution: Take ownership of data analysis projects, leveraging AI/ML to solve business problems and drive results in sales, billing, payments, and customer experience.
Mentorship & Best Practices: Lead by example in executing analytics tasks with high quality, sharing best practices, and mentoring junior analysts to build a strong analytics foundation.
Collaboration:
Work closely with cross-functional teams to provide data-driven insights that align with business objectives, ensuring actionable outcomes.
AI & Data Insights: Apply machine learning models and predictive analytics to identify patterns, improve decision-making, and automate reporting.
Problem Solving & Communication: Break down complex problems into structured analysis, and communicate insights clearly to the stakeholders.
Preferred Qualifications:
Education :
Bachelor's degree in a related field (e.g., Engineering, Data Science, Statistics, Business Analytics)
Experience:
8-10 years in data analysis, with demonstrated ownership of end-to-end deliverables with clear business impact
Business Knowledge: Strong understanding of business processes, including acquisition cycles, customer experience, workforce management.
Technical Expertise
Proficiency in data analysis and visualization tools (e.g., SQL, Python, Pyspark, Tableau, Power BI, Excel)
Understanding of ETL pipelines, data warehousing, and cloud-based AI analytics platforms
Proficiency in A/B testing, cohort analysis, MECE methodologies, and predictive modelling
AI/ML Knowledge
Solid foundation in applying AI/ML techniques (Regression, Random Forest, Boosting techniques) to business data, including predictive analytics and anomaly detection.
Basic experience with machine learning tools and frameworks such as Python, Scikit-Learn, or similar
Familiarity with Generative AI tools and their application in business intelligence and reporting is a plus
Mentorship & Leadership: Proven ability to mentor junior analysts and foster a collaborative, learning-oriented team environment.
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