Translate ambiguous business problems into structured analytical and model-driven solutions aligned with business objectives
Partner with stakeholders to define hypotheses, success metrics, and analytical approaches for decision-making
Perform deep exploratory data analysis (EDA), hypothesis testing (A/B testing, what-if analysis), and scenario modeling
Develop statistical, machine learning, and hybrid AI models to solve complex business problems such as churn prediction, revenue optimization, anomaly detection, and operational efficiency
Build scalable analytical workflows and feature engineering pipelines using Python, SQL, and BigQuery
Design and implement predictive models using classical ML and advanced analytics techniques
Identify inefficiencies and anomalies in business systems using KPI-based monitoring and outlier detection methods
Ensure data consistency, accuracy, and reliability through robust validation and governance frameworks
Monitor model performance, implement feedback loops, and continuously improve analytical solutions
Collaborate with data engineering, platform, SRE, and business teams to operationalize AI-driven insights
Develop dashboards and data visualizations to communicate insights effectively to technical and non-technical stakeholders
Drive measurable business impact through data science initiatives and analytical storytelling
Mentor junior analysts and promote a strong data-driven culture within the organization
