Manager - Analytics

Job Context:

The role requires working across business functions to design, develop, and deploy analytics solutions using advanced data science and machine learning techniques. It involves translating business requirements into analytical problem statements, building scalable data pipelines, and enabling insights through dashboards and reporting tools. The role also requires continuous monitoring and optimization of models, ensuring alignment with business objectives and adoption of best practices in analytics and model lifecycle management.

The individual will collaborate with cross-functional teams, including business stakeholders, data engineers, and technology teams, to deliver actionable insights and scalable solutions. Additionally, the role demands staying updated with emerging trends in AI/ML, including NLP and LLMs, to drive innovation.

Major Challenges:

• Data Complexity & Quality: Managing large, complex datasets from multiple sources with varying levels of data quality
• Business Translation: Converting ambiguous business problems into structured analytical solutions
• Scalability: Building reusable and scalable analytics frameworks and pipelines
• Model Lifecycle Management: Ensuring continuous monitoring, validation, and performance tracking of models
• Technological Advancements: Keeping pace with rapidly evolving tools, platforms, and AI/ML advancements
• Stakeholder Alignment: Managing expectations and ensuring adoption of analytics solutions across functions

Key Result Areas:

• Lead development and deployment of analytics solutions across business use cases
• Build, validate, and optimize machine learning models
• Translate business requirements into analytical frameworks
• Define problem statements and solution approaches
• Track model performance and ensure continuous improvement
• Implement best practices in model validation and monitoring
• Develop dashboards and reporting solutions using Power BI and DAX
• Deliver actionable insights to stakeholders
• Build scalable and reusable data science frameworks and pipelines
• Work with cross-functional teams to deliver business insights
• Communicate results effectively
• Stay updated with AI/ML trends including NLP and LLMs
• Drive adoption of modern analytics tools and techniques