New opportunity

Decision Intelligence Lead

Agilent Technologies, Inc. · Santa Clara, United States

About this role

The Decision Intelligence Lead, within the Operations Intelligence & Analytics organization, drives the product strategy, roadmap, and delivery of executive analytics solutions that translate Global Operations initiatives into measurable business outcomes. This role transforms complex operational data into actionable insights delivered through dashboards, scorecards, and interactive applications that improve decision-making, performance, and efficiency across the enterprise. Responsibilities: Build and own the product roadmap, including executive insights dashboards and balanced scorecard reporting. Apply product strategy and UX principles to design scalable data products. Develop decision intelligence products within Power BI and Microsoft Fabric. Build interactive scenario planning applications using predictive models. Automate data workflows using Python and SQL. Integrate ERP data from SAP and Oracle. Apply AI/ML techniques to enhance operational forecasting and risk identification. Use DevOps tools including GitHub, Confluence, and Jira. Qualifications: Bachelor's degree in Data Engineering, Data Science, Computer Science, Statistics, or related field (Master's preferred). 8+ years of experience in data science, business intelligence, or analytics. Expertise in Power BI (DAX) and proficiency in Python (pandas, NumPy) and SQL. Experience with front-end frameworks like Power Apps, Plotly Dash, or React.js. Experience with unified analytics platforms like Microsoft Fabric or Databricks. Preferred Qualifications: Experience supporting global supply chain, manufacturing, quality, or regulatory operations. Familiarity with GenBI capabilities (e.g., Copilot) and GenAI development. Practical experience across the ML lifecycle (scikit-learn, XGBoost). Relevant certifications such as MS DP-600 or MS DP-700. Additional Details: Full-time position with a pay range of $124,160 - $232,800/yr plus bonus, stock, and benefits. Travel required 10% of the time.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Power BIMicrosoft FabricPythonSQLDAXPredictive AnalyticsMachine LearningData ScienceBusiness IntelligenceDevOpsGitHubJiraConfluenceSAPOracleDatabricksAWSSnowflakeReact.jsPower AppsPlotly DashGenAI

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Executive Analytics Product Strategy
The role requires owning the end-to-end product roadmap for executive dashboards and balanced scorecards. You must demonstrate the ability to translate complex Global Operations initiatives into intuitive, user-centric data products that directly influence executive decision-making.
Operational Data Integration and Automation
A core priority is building scalable data pipelines that integrate disparate ERP data from SAP and Oracle. You must be proficient in automating these workflows using Python and SQL to ensure consistent, high-quality data for decision intelligence applications.
Predictive and Scenario-Based Modeling
You are expected to move beyond descriptive reporting by developing interactive applications that incorporate predictive models. This involves using AI/ML techniques to support scenario planning, risk identification, and operational forecasting.

Likely challenges to think through

Navigating Matrixed Organizational Complexity
Working across Supply Chain, Manufacturing, and Regulatory functions in a large global organization involves competing stakeholder requirements. Prepare by highlighting your experience in managing cross-functional alignment and prioritizing features that provide the highest business value.
Balancing Technical Depth with UX Design
The role demands both advanced backend data engineering (Microsoft Fabric, Python) and front-end UX design. The challenge is ensuring that complex predictive models remain accessible and actionable for non-technical executive users. Prepare to discuss your approach to data storytelling and intuitive interface design.

Practice your answers

Technical practice

Designing Scalable Decision Intelligence Products

What to coverExplain your approach to balancing product strategy with UX principles when building executive dashboards. Focus on how you ensure data products are intuitive for non-technical stakeholders while remaining scalable for complex enterprise data.

Power BI and Microsoft Fabric Integration

What to coverDescribe your experience building semantic models and KPI frameworks within Microsoft Fabric. Discuss how you leverage DAX for complex time-intelligence calculations and ensure performance optimization in large-scale reporting environments.

Predictive Modeling for Scenario Planning

What to coverHow do you incorporate predictive models into interactive applications to support trade-off analysis? Provide examples of how you have used tools like Python (pandas, NumPy) or ML frameworks (scikit-learn, XGBoost) to enhance operational forecasting.

Data Wrangling and ERP Integration

What to coverDiscuss your methodology for integrating disparate data sources, specifically SAP and Oracle, into a cohesive analytics platform. Address how you handle data quality, transformation, and automation using Python and SQL.

ML Lifecycle Management

What to coverDetail your process for managing the end-to-end machine learning lifecycle, from feature engineering and model selection to deployment and performance monitoring. Suggestion: Focus on how you maintain model accuracy over time in a production environment.

DevOps in Analytics Development

What to coverHow do you utilize GitHub, Jira, and Confluence to manage the product development lifecycle? Explain how these tools support your agile delivery, version control, and documentation processes for data products.

Front-end Frameworks for Data Applications

What to coverCompare your experience using low-code tools like Power Apps against full-code frameworks like Plotly Dash or React.js for building interactive data applications. Explain the criteria you use to select the appropriate framework for a specific business requirement.

Leveraging GenAI in BI

What to coverDescribe your familiarity with GenBI capabilities, such as Copilot, and the development process for GenAI applications. Discuss how these technologies can be integrated into existing BI workflows to improve user accessibility and insight generation.

Behavioural practice

Translating Business Needs for Executives

What to coverDescribe a situation where you had to translate complex operational data into a strategy-influencing insight for executive leadership. Use the STAR method to explain how you communicated the findings and the resulting impact on business outcomes.

Navigating Matrixed Organizations

What to coverProvide an example of working within a large, matrixed organization to deliver a cross-functional analytics project. Explain how you managed stakeholder expectations across different departments like Supply Chain or Quality.

Product-Oriented Analytics Approach

What to coverTell me about a time you managed an analytics solution from initial strategy and requirements through to deployment and continuous enhancement. How did you handle feedback and iterate on the product to ensure it remained user-centric?

Managing Conflicting Priorities

What to coverDescribe a time when you had to balance competing demands on your product roadmap. How did you prioritize features to ensure the most significant measurable business outcomes were achieved?

Sharpen your knowledge

Topics to prepare

Product-Oriented Analytics Strategy
The role requires managing an analytics roadmap and UX-focused data products. Exercise: Draft a product roadmap for an executive dashboard that tracks supply chain KPIs, focusing on user-centric design and business outcomes rather than just technical metrics.
Predictive Modeling for Operational Decision Support
The role involves building scenario planning applications. Exercise: Review the ML lifecycle (feature engineering to deployment) and design a conceptual framework for a predictive model that identifies supply chain risks or forecasts operational performance.

Skills to revise

Power BI and DAX Proficiency
The role requires advanced semantic modeling and KPI development. Exercise: Practice creating complex DAX measures for time-intelligence calculations and optimizing semantic models for performance in a large-scale enterprise environment.
Python for Data Automation
The role requires automating data workflows and wrangling disparate sources. Exercise: Build a script using pandas to automate the extraction and transformation of data from multiple CSV or SQL sources, simulating an ERP integration scenario.
SQL for ERP Data Integration
The role involves integrating data from SAP and Oracle. Exercise: Write complex SQL queries to join and aggregate data from disparate ERP tables, focusing on data cleaning and preparation for downstream analytics products.
DevOps for Analytics Lifecycle
The role requires using GitHub, Confluence, and Jira for product management. Exercise: Create a mock project board in Jira or a documentation template in Confluence that tracks the development lifecycle of a data product from requirements gathering to deployment.

Your preparation checklist

Prepare a product-oriented analytics case study
Develop a presentation showcasing a past project where you owned the full lifecycle of an analytics product. Highlight how you translated complex operational data into an executive-level dashboard or scorecard, specifically detailing your use of UX principles to ensure the tool was intuitive and actionable for leadership.
Demonstrate technical proficiency in the Microsoft data stack
Prepare to discuss your experience with Power BI and Microsoft Fabric. Be ready to explain how you have used DAX for complex time-intelligence calculations and how you have integrated disparate data sources, such as SAP or Oracle, into a unified semantic model.
Showcase predictive modeling and automation workflows
Create a summary of a project where you applied Python (pandas, NumPy) and SQL to automate data pipelines. If applicable, describe how you incorporated ML models (e.g., scikit-learn, XGBoost) to solve a specific operational forecasting or risk identification challenge.
Formulate questions regarding the Operations Intelligence roadmap
Prepare thoughtful questions for the interviewers about the current maturity of their Global Operations data products, the primary challenges in integrating their ERP systems, and how the team balances immediate executive reporting needs with long-term predictive analytics development.

Availability last checked: Oct 18, 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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