Our Client Currently looking for Business Intelligence Analyst

Key Responsibilities

• Build end-to-end analytical models to evaluate customer lifecycle, including acquisition → activation → retention → monetization.
• Leverage outputs from ML models (churn, LTV, propensity scoring) to drive predictive analytics and decision frameworks.
• Develop advanced segmentation models using clustering techniques (k-means, hierarchical clustering, DBSCAN) and behavioral feature engineering.
• Perform deep funnel analysis using event-level data (clickstream, product interactions) to identify inefficiencies and optimization opportunities.
• Design and evaluate experimentation frameworks (A/B testing, multivariate testing) with statistical rigor.
• Build data pipelines for analytics use cases using SQL and Python, ensuring scalability and reproducibility.
• Work with large datasets from multiple sources (marketing platforms, product analytics tools, trading systems).
• Apply attribution modeling techniques (rule-based, probabilistic, MMM) to evaluate marketing performance.
• Translate complex datasets and model outputs into clear, commercially actionable insights.
• Automate recurring analyses and reporting using Python and workflow orchestration tools.
• Partner with business, product, marketing, and technology teams to identify opportunities where AI, machine learning, and advanced analytics can drive growth, engagement, retention, and operational improvements.
• Translate complex datasets, predictive model outputs, and AI-generated insights into clear, commercially actionable recommendations for senior stakeholders.
• Automate recurring analyses, reporting, forecasting, and monitoring processes using Python, workflow orchestration tools, and AI-powered analytics solutions.
• Monitor model performance, data quality, and business impact, continuously refining analytical frameworks and AI solutions to maximize effectiveness and accuracy.

Technical Requirements

• Strong SQL expertise with experience querying and transforming large datasets.
• Advanced Python skills for data analysis (pandas, NumPy, SciPy, statsmodels).
• Experience with statistical modeling and hypothesis testing.
• Familiarity with machine learning concepts and interpreting model outputs.
• Experience with data visualization tools (Tableau, Power BI, Looker).
• Exposure to product analytics tools (Amplitude, Mixpanel, GA4).
• Strong understanding of cohort analysis, retention curves, LTV modeling and funnel optimization.
• Ability to evaluate model performance using metrics such as Precision, Recall, F1 Score, ROC-AUC, Lift, and Incrementality.
• Experience leveraging AI-powered analytics tools and copilots to automate data exploration, reporting, forecasting, and anomaly detection.
• Experience with A/B testing frameworks and experimental design.
• Knowledge of marketing analytics concepts (ROI, ROAS, attribution models).
• Ability to work with semi-structured and event-driven data.

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