• Translate complex business problems into structured, data- and model-driven narratives. Partner with stakeholders to frame ambiguous problem statements, perform deep exploratory Data Analysis (EDA), Visualizations, Hypothesis A/B Testing/ What-if analysis and scenario modeling, Algorithm development to identify inefficient sectors (DL/UL) by flagging outliers using performance KPIs.

• Communicate statistically sound, model-backed insights that directly influence strategic and operational decisions.

• Apply advanced statistical techniques, classical ML, and modern AI approaches to forecast outcomes and recommend next-best actions. 

• Experience in driving initiatives through a rigorous lifecycle—problem formulation → hypothesis generation → EDA → feature engineering → modeling → evaluation → visualization → measurable business impact—ensuring scientific rigor, interpretability, and alignment with business objectives.

• Develop, evaluate, and iterate on analytical, machine learning, and hybrid AI models to uncover patterns, trends, and anomalies, solving complex problems such as churn prediction, revenue optimization, network efficiency, and operational optimization.

• Demonstrate strong hands-on expertise in BigQuery, SQL, and Python to build scalable data pipelines, feature stores, and analytical workflows, ensuring performance, reproducibility, and accuracy on large-scale datasets.

• Ensure consistency and reliability across diverse data sources through strong data validation, monitoring, and governance practices. Maintain trust in analytical and AI-driven outcomes through robust data quality checks, model validation, and ongoing performance monitoring.

• Collaborate with cross-functional teams (data engineering, SRE, platform, and business teams) to operationalize AI-driven insights, ensure reliability, and deliver measurable business impact.

• Drive continuous improvement initiatives by integrating feedback loops, monitoring KPIs on data reliability aligned to enterprise goals.

• Mentorship & Collaboration – Guide junior analysts, promote knowledge sharing, and foster a culture of analytical excellence across teams.

• Analytical Mindset & Self-Starter – Proactive in identifying opportunities, framing problems, and communicating insights in a business-first language, bridging the gap between data science and strategy.

• Data Visualization & Dashboards – Transform raw numbers into intuitive dashboards and visual stories that resonate with both technical and non-technical audiences, enabling faster decisions.

• Strong proficiency in Advanced SQL with experience in writing optimized queries for large datasets.          

• Expertise in data analytics with a solid understanding of statistical methods and data interpretation.

• Exposure to Data Science concepts, including predictive modeling and machine learning techniques.

• Hands-on experience with Python, R, or similar analytical tools is a plus.

• Experience working with large-scale datasets and business intelligence tools.

• Strong problem-solving abilities with the capability to translate business problems into analytical solutions.

• Excellent communication and presentation skills to convey insights effectively to stakeholders.