BI Manager
Skills
About This Role
Key Responsibilities
- Lead the BI team in planning, developing, and delivering high-impact dashboards, reports, and analytics solutions
• Business Intelligence Leadership
- Own the end-to-end Business Intelligence strategy and execution
- Establish BI as a core decision-support capability across the company
- Define and govern KPIs, metrics, and analytical standards
- Ensure insights consistently explain what happened, why it happened, and what to do next
- Lead executive and operational reporting and insight narratives
Analytics, Forecasting & Insights
- Oversee trend analysis, root-cause analysis, and correlation studies
- Drive forecasting, projections, and scenario analysis for planning and expectation-setting
- Ensure analytical outputs are explainable, assumption-driven, and decision-ready
Applied AI and Machine Learning Leadership
- Lead and mentor ML engineers to design, build, and deploy applied machine learning models
- Own the applied ML and MLOps roadmap, aligned with BI and business priorities
- Guide the development of models such as:
- Recommendation and pattern discovery models
- Anomaly detection
- Define and enforce MLOps standards, including:
- Model versioning and lifecycle management
- Deployment and rollback strategies
- Monitoring model performance, drift, and data quality
- Retraining and validation processes
- Ensure ML models are:
- Business-driven and use-case oriented
- Explainable and interpretable
- Observable and maintainable in production
- Cross-Functional Leadership
- Act as the intelligence partner for Product, Finance, Operations, and Engineering
- Work closely with Data Engineering, Backend Engineering, and Platform teams to operationalize insights and ML models
Technical & Analytical Requirements
- Strong understanding of Business Intelligence and analytics practices
- Hands-on experience with BI tools (Power BI preferred)
- Strong analytical foundation, including:
- Correlation vs causation
- Forecasting and scenario modeling
- Root-cause analysis
- Solid understanding of data warehousing concepts (BigQuery preferred)
- Working knowledge of applied machine learning, including:
- Supervised and unsupervised learning approaches
- Feature engineering and model evaluation
- Model explainability techniques
- Working knowledge of MLOps concepts, including:
- Model deployment and lifecycle management
- Monitoring for performance degradation and data drift
- Retraining strategies and validation workflows
- Collaboration with engineering teams on CI/CD and production readiness
- Ability to guide ML engineers on both modeling and operationalization, without acting as a research data scientist
- Bachelor s degree in Computer Science, Information Systems, or a related field..
- 5+ years of experience in business intelligence, data analytics, or data management.
- Applied ML models are deployed, monitored, and reliably operated in production
- MLOps practices ensure models remain accurate, observable, and trusted over time
- ML outputs are explainable and confidently used by business stakeholders
- BI remains the starting point for decisions, with ML and MLOps extending insight into action
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