About the job
Job Purpose:
The Business Intelligence Manager is responsible for overseeing the company's Business Intelligence function, transforming data into reliable insights that inform strategic and operational decisions. This role spearheads the implementation of BI across the organization, emphasizing correlation, causation, forecasting, and insightful storytelling. Additionally, the role involves leading applied AI and machine learning initiatives, supervising ML engineers to create, deploy, and manage high-quality, business-oriented models, while ensuring adherence to MLOps practices that guarantee reliability, scalability, and trust.
Key Responsibilities
- Lead the BI team in strategizing, developing, and delivering impactful dashboards, reports, and analytics solutions.
- Champion Business Intelligence leadership initiatives.
- Own the comprehensive Business Intelligence strategy and its execution.
- Establish BI as a fundamental decision-support capability within the organization.
- Define and oversee KPIs, metrics, and analytical standards.
- Ensure that insights consistently clarify what occurred, the reasons behind it, and the subsequent steps.
- Lead both executive and operational reporting and insight narratives.
Analytics, Forecasting & Insights
- Supervise trend analysis, root-cause analysis, and correlation studies.
- Drive forecasting, projections, and scenario analysis for planning purposes.
- Ensure analytical outputs are understandable, assumption-based, and ready for decision-making.
Applied AI and Machine Learning Leadership
- Guide and mentor ML engineers in designing, constructing, and deploying applied machine learning models.
- Oversee the applied ML and MLOps roadmap, ensuring alignment with BI and business objectives.
- Assist in developing models including:
- Recommendation and pattern discovery models.
- Anomaly detection systems.
- Establish and uphold MLOps standards, which encompass:
- 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-focused and use-case driven.
- Explainable and interpretable.
- Observable and maintainable in production.
- Cross-Functional Leadership
- Act as the intelligence partner for Product, Finance, Operations, and Engineering teams.
- Collaborate closely with Data Engineering, Backend Engineering, and Platform teams to operationalize insights and ML models.
Technical & Analytical Requirements
- In-depth understanding of Business Intelligence and analytics methodologies.
- Practical experience with BI tools (Power BI preferred).
- Robust analytical foundation including:
- Correlation vs causation.
- Forecasting and scenario modeling.
- Root-cause analysis.
- Strong comprehension of data warehousing principles.
