BI Manager

• 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

• 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

• 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

• 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

Gathern