Advanced Analytics Business Partner

Job Title

Advanced Analytics Business Partner

Job Description

Job title: Advanced Analytics Business Partner

Your role:

The Advanced Analytics Business Partner is accountable for end-to-end sell-out and commercial performance analytics, combining deep business understanding with strong hands-on analytical, data, and AI capabilities. The role goes beyond traditional reporting—focusing on insight creation, decision support, and AI-enabled analytics solutions that directly influence business outcomes.

This role owns performance dashboards and Commercial data sets (e.g., GfK, Circana) while actively designing, building, and automating analytics products using Python, SQL, traditional machine learning techniques, and GenAI tools. Acting as a trusted thought partner to BU leadership, Sales, Marketing, and Finance, the role translates complex market and customer data into clear “so-what / now-what” insights.

Operating with a builder and translator mindset, the individual rapidly develops MVP analytics solutions, leverages AI to accelerate insight generation, and ensures strong product engineering fundamentals, data quality, and governance. The role plays a critical part in establishing a single source of truth for performance management across markets and channels while elevating analytics maturity from descriptive reporting to predictive and insight-led decision making.

Sell-out Performance & Advanced Market Analytics

• Own end-to-end sell-out performance analytics across markets, channels, and key customers.

• Lead advanced analysis of market share, growth drivers, pricing, mix, and distribution using Python- and SQL-based analytical workflows.

• Apply strong statistical and analytical thinking to identify trends, anomalies, risks, and opportunities—connecting multiple data points into coherent business narratives.

• Present performance insights in weekly, monthly, and quarterly leadership forums with a clear decision-oriented storyline.

Analytics Products, Dashboards & Data Engineering Mindset

• Own sell-out and customer performance dashboards as analytics products, not static reports.

• Build, automate, and enhance dashboards using scalable SQL logic, Python transformations, and reusable analytical components.

• Ensure accuracy, consistency, and first-time-right delivery through strong validation, reconciliation, and governance practices.

• Continuously evolve analytics products based on business feedback, usage patterns, and emerging performance gaps.

Syndicated Data Ownership & Governance

• Act as the business owner for syndicated sell-out data sources (e.g., GfK, Circana, Nielsen).

• Own methodologies, assumptions, definitions, and limitations of external data sources.

• Partner with data engineering and IT teams to ensure data quality, harmonization, and regional alignment.

• Build trust in analytics outputs through transparent logic and clear communication of data caveats.

Advanced Analytics, ML & GenAI Enablement (Pragmatic, Value-Driven)

• Drive analytics maturity by shifting from descriptive reporting to diagnostic, predictive, and insight-led decision support.

• Apply traditional machine learning techniques (e.g., regression, clustering, forecasting, anomaly detection) selectively to enhance performance insights—not for complexity’s sake.

• Leverage GenAI tools (e.g., ChatGPT, Claude) to:

  • Accelerate hypothesis generation and insight discovery
  • Support rapid analysis, scenario exploration, and internal analytics tool building
  • Improve productivity, documentation, and stakeholder communication

• Operate with a build-fast, learn-fast MVP mindset, balancing speed with sound product engineering fundamentals.

Business Partnership, Leadership Exposure & Decision Support

• Serve as a trusted analytics and thought partner to BU leadership, Sales, Marketing, and Finance.

• Translate complex analytics into clear recommendations that influence decisions and actions.

• Support performance reviews, planning cycles, and ad-hoc leadership requests with speed, rigor, and confidence.

• Proactively challenge assumptions and guide stakeholders using fact-based, data-driven insights.

Skills & Competencies

• Strong hands-on analytics skills: Python for analysis and automation, SQL for data extraction and transformation, and solid statistical foundations.

• Working knowledge of traditional machine learning techniques, with the ability to apply them pragmatically to business problems.

• Experience leveraging GenAI tools (e.g., ChatGPT, Claude, or similar) to accelerate insight generation, analysis, and internal tool development.

• Strong business acumen with the ability to connect disparate data points, create compelling narratives, and influence senior stakeholders.

• High ownership, curiosity, and a builder mindset—comfortable operating in ambiguous environments and rapidly turning ideas into MVP solutions.

Education Requirements

• Bachelor’s degree in Engineering, Computer Science, Statistics, Economics, Mathematics, or a related quantitative field.

• Master’s degree preferred (e.g., Data Analytics, Business Analytics, Applied Statistics, Economics, AI, or MBA with strong analytics focus).

• Continuous learning mindset expected, with demonstrated upskilling in advanced analytics, AI, or data engineering concepts (formal or informal).

You're the right fit if:

• 8–12+ years of experience in Performance Analytics, Commercial Analytics, Advanced Analytics, Decision Intelligence, or similar roles.

• Proven track record of owning end-to-end analytics domains, not just contributing to isolated analyses or consuming pre-built reports.

• Strong hands-on experience working with syndicated market data (e.g., GfK, Circana, Nielsen, or equivalent), including methodologies, assumptions, and limitations.

• Demonstrated experience building analytics products or solutions (dashboards, analytical models, automation pipelines, internal tools) rather than static reporting outputs.

• Practical experience applying advanced analytical techniques (e.g., regression, forecasting, segmentation, anomaly detection) to real business problems—without over-engineering.

• Hands-on exposure to AI-enabled analytics, including the use of GenAI tools (e.g., ChatGPT, Claude, or similar) to accelerate insight generation, analysis, or productivity.

• Strong experience partnering with senior business stakeholders (BU leaders, Sales, Marketing, Finance), influencing decisions through insight-led storytelling.

• Experience operating in complex, matrixed organizations, balancing speed, rigor, and stakeholder expectations.

• Demonstrated ability to operate with a builder and MVP mindset, rapidly prototyping solutions and iterating based on feedback while ensuring data quality and governance.

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How we work together We believe that we are better together than apart. For our office-based teams, this means working in-person at least 3 days per week. Onsite roles require full-time presence in the company’s facilities. Field roles are most effectively done outside of the company’s main facilities, generally at the customers’ or suppliers’ locations. this role is an office role.

About Philips We are a health technology company. We built our entire company around the belief that every human matters, and we won't stop until everybody everywhere has access to the quality healthcare that we all deserve. Do the work of your life to help the lives of others.

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• Discover our rich and exciting history.

• Learn more about our purpose. If you’re interested in this role and have many, but not all, of the experiences needed, we encourage you to apply. You may still be the right candidate for this or other opportunities at Philips. Learn more about our culture of impact with care here.