Data Scientist - Performance Analytics

Philips Iberica SAU; Bengaluru; On-site; INR 2,000,000 - 3,600,000; Full time

Job summary

Philips in Bengaluru is seeking a Senior Data Scientist - Performance Analytics to drive hands-on data science, analytics-product development and commercial insight. You will partner with business teams and own analytics workstreams from framing and data prep to deployment and adoption.

You will build dashboards and predictive models within governance standards, leveraging Python, SQL and enterprise data platforms to enable faster, evidence-based decisions across markets.

Qualifications

• 4–7 years in data science, analytics or BI with business impact.
• Ability to translate business questions into analytical solutions.
• Experience developing dashboards, predictive models or AI-enabled tools.
• Comfort working with cross-functional stakeholders and complex datasets.

Responsibilities

• Translate business priorities into analytical requirements and success measures.
• Analyze performance metrics across market, revenue, margin and operations.
• Develop and deploy analytics products, dashboards and AI-enabled tools.
• Collaborate with Data Engineering and IT to ensure trusted data.
• Drive adoption through demonstrations, documentation and training.

Skills

Python

SQL

Dashboard design

Analytics

Stakeholder comms

Education

Bachelor's or Master's in CS/DS/AI/Applied Math/Statistics

Tools

Power BI

Qlik

Databricks

Azure

Job description

Data Scientist - Performance Analytics

Job Description

The Senior Data Scientist - Performance Analytics combines hands‑on data science, analytics-product development and commercial understanding to help Philips monitor, explain and predict business performance. The role partners with assigned Business, Regional or Functional teams and owns defined analytics workstreams from problem framing and data preparation through deployment and adoption. Working within established architecture and governance standards, the role develops trusted dashboards, predictive models and AI-enabled decision‑support tools that improve the speed and quality of business decisions.

Job Responsibilities

1. Performance Analytics and Business Partnering

• Translate business priorities and performance questions into clear analytical requirements and success measures.
• Analyze market share, sell‑in, sell‑out, revenue, margin, customer, channel, portfolio and operational performance.
• Identify drivers, risks and opportunities, and communicate findings through concise narratives and practical recommendations.
• Support business reviews, planning cycles and performance‑management processes with evidence‑based insights.

2. Data Foundations and Analytics Products

• Prepare, integrate and validate data from approved internal and external sources using Python, SQL and enterprise data platforms.
• Collaborate with Data Engineering, IT and business teams to improve trusted datasets in ADL and related environments.
• Build and enhance dashboards and analytical products with diagnostics, alerts, benchmarks and decision‑support features.
• Document data sources, business rules, calculations, assumptions and known limitations.

3. Advanced Analytics and AI

• Apply statistical and machine‑learning methods, including regression, classification, clustering, forecasting and anomaly detection.
• Develop, validate and monitor models for accuracy, stability, explainability and business relevance.
• Contribute to AI‑enabled solutions such as automated commentary, conversational analytics, retrieval‑based tools and analytical agents.
• Follow Philips standards for responsible AI, security, privacy and model controls.

4. Deployment and Engineering

• Work with Lead Data Scientists, data engineers, IT and platform teams to productionize analytical solutions.
• Write clean, modular and reusable code using version control, testing and peer‑review practices.
• Support user acceptance testing, deployment, monitoring and resolution of data or technical issues.
• Contribute reusable components and improvements to shared analytical methods and frameworks.

5. Adoption and Knowledge Sharing

• Drive adoption through demonstrations, training, user documentation and regular stakeholder engagement.
• Gather user feedback and translate it into product improvements.
• Share methods, code and lessons with the analytics community, and coach junior colleagues where appropriate.

Success Measures

• Timely and high‑quality delivery of assigned analytics products and workstreams.
• Accuracy, reliability and business relevance of analytical and AI outputs.
• Adoption and active use of dashboards, models and AI‑enabled tools.
• Reduction in manual analysis, repetitive reporting and duplicated solutions.
• Measurable contribution to faster, higher‑quality business decisions and outcomes.
• Compliance with data, technology and responsible‑AI standards, supported by effective cross‑functional collaboration.

You're the right fit if:

Experience

• Approximately 4-7 years of relevant experience in data science, advanced analytics, commercial analytics, business intelligence or a related discipline.
• Experience translating business questions into structured analytical solutions and clear recommendations.
• Experience developing and deploying dashboards, predictive models, data products, automated insights or AI‑enabled tools.
• Experience working with complex datasets and cross‑functional, matrixed or multi‑market stakeholders.

Technical and Analytical Skills

• Strong proficiency in Python and SQL, with practical experience in data preparation, exploratory analysis and statistical modelling.
• Working knowledge of machine‑learning methods and model evaluation.
• Experience with a business‑intelligence or visualization platform such as Power BI or Qlik.
• Familiarity with cloud data platforms, data lakes or enterprise analytics environments such as ADL.
• Working knowledge of version control, testing, documentation and reproducible analytical‑development practices.
• Basic understanding of generative AI, large language models, retrieval‑based solutions and responsible‑AI principles.

Business and Partnering Skills

• Sound commercial and financial understanding, supported by strong problem‑solving and root‑cause‑analysis skills.
• Ability to connect analytical findings with business performance and practical management actions.
• Ability to communicate complex technical concepts clearly to non‑technical stakeholders.
• Ability to independently manage a defined analytics workstream and collaborate across Business, Analytics, IT, Finance and Data Engineering teams.

Minimum Required Education

Bachelor's or Master's degree in Computer Science, Data Science, Econometrics, Artificial Intelligence, Applied Mathematics, Statistics, Engineering, Business Analytics or a related quantitative discipline. Equivalent practical experience may also be considered.

Preferred Experience

• Experience in commercial, sales, marketing, finance, customer, pricing or market‑performance analytics.
• Experience with Azure, Databricks, ADL or comparable cloud data platforms.
• Exposure to production machine learning, MLOps, generative‑AI applications or analytical agents.
• Experience in a global consumer, healthcare, retail, technology or manufacturing organization.

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.

About Philips

Are you ready to do the work of your life to help the lives of others? Learn more about our business, discover our rich and exciting history and learn more about our purpose. Learn more about our culture of impact with care.