Job Purpose:
The Business Intelligence Manager owns the company’s Business Intelligence function and is accountable for turning data into trusted insights that drive strategic and operational decisions. This role leads the activation of BI across the organization, focusing on correlation, causation, forecasting, and storytelling.The role also leads applied AI and machine learning initiatives, managing ML engineers to build, deploy, and operate production-grade, business-driven models, with clear ownership of applied MLOps practices to ensure reliability, scalability, and trust.
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
Requirements
- 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