Sr Lead Data Analyst

Job Description As a Senior Lead Data Analyst at Airtel, you will be instrumental in driving data-backed decision-making by providing actionable insights across various business functions. You will independently analyze complex business data related to sales, revenue, customer experience, and financial operations such as billing, payments, and collections. Additionally, you will contribute to developing AI-driven solutions aimed at enhancing business performance and streamlining processes. Key Responsibilities: - End-to-End Analytics Execution: Take charge of data analysis projects, utilizing AI/ML techniques to address business challenges and achieve results in sales, billing, payments, and customer experience. - Mentorship & Best Practices: Lead the way in executing analytics tasks with excellence, share best practices, and guide junior analysts to establish a robust analytics foundation. Collaboration: - Work closely with cross-functional teams to offer data-driven insights aligned with business objectives, ensuring actionable outcomes. - AI & Data Insights: Apply machine learning models and predictive analytics to identify patterns, enhance decision-making, and automate reporting. - Problem Solving & Communication: Break down intricate problems into structured analyses and effectively communicate insights to stakeholders. Preferred Qualifications: - Education: Bachelors degree in a related field (e.g., Engineering, Data Science, Statistics, Business Analytics) - Experience: 8-10 years in data analysis, demonstrating ownership of end-to-end deliverables with tangible business impact - Business Knowledge: Solid understanding of business processes, encompassing acquisition cycles, customer experience, and workforce management Technical Expertise: - Proficiency in data analysis and visualization tools like 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 modeling AI/ML Knowledge: - Strong 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 advantageous Mentorship & Leadership: - Demonstrated ability to mentor junior analysts and cultivate a collaborative, learning-oriented team environment. As a Senior Lead Data Analyst at Airtel, you will be instrumental in driving data-backed decision-making by providing actionable insights across various business functions. You will independently analyze complex business data related to sales, revenue, customer experience, and financial operations such as billing, payments, and collections. Additionally, you will contribute to developing AI-driven solutions aimed at enhancing business performance and streamlining processes. Key Responsibilities: - End-to-End Analytics Execution: Take charge of data analysis projects, utilizing AI/ML techniques to address business challenges and achieve results in sales, billing, payments, and customer experience. - Mentorship & Best Practices: Lead the way in executing analytics tasks with excellence, share best practices, and guide junior analysts to establish a robust analytics foundation. Collaboration: - Work closely with cross-functional teams to offer data-driven insights aligned with business objectives, ensuring actionable outcomes. - AI & Data Insights: Apply machine learning models and predictive analytics to identify patterns, enhance decision-making, and automate reporting. - Problem Solving & Communication: Break down intricate problems into structured analyses and effectively communicate insights to stakeholders. Preferred Qualifications: - Education: Bachelors degree in a related field (e.g., Engineering, Data Science, Statistics, Business Analytics) - Experience: 8-10 years in data analysis, demonstrating ownership of end-to-end deliverables with tangible business impact - Business Knowledge: Solid understanding of business processes, encompassing acquisition cycles, customer experience, and workforce management Technical Expertise: - Proficiency in data analysis and visualization tools like 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 modeling AI/ML Knowledge: - Strong 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 Genera