Data Scientist

Philip Morris International; Jakarta Pusat; Hybrid; IDR 260,000,000 - 520,000,000; Full time

Job summary

Philip Morris International is seeking a Full Stack Data Science and Analytics professional in Jakarta for a hybrid role bridging statistical modeling and product growth. You will manage end-to-end data lifecycles, from ingestion and quality to building predictive models and leading product experimentation to drive engagement and efficiency.

The ideal candidate has 5+ years in Data Science or Product Analytics, expert SQL and Python, and strong experience with GA4 and Looker.

Qualifications

• Bachelor’s or Master’s in Data Science, Statistics, Mathematics, Economics, or a related quantitative field.
• Minimum of 5 years in Data Science, Product Analytics, or a closely related quantitative field.
• Strong product-minded approach with ability to translate data into business insights.
• Advanced SQL and Python required.
• Expertise in GA4 and Looker for product analytics.
• Deep knowledge of A/B Testing and experimental design.
• Proficiency in time-series analysis (Prophet, ARIMA) and growth modeling.

Responsibilities

• Design, implement, and maintain predictive models and data analyses.
• Apply analysis across Market Economics, Supply Chain, Marketing/Advertising, and Research.
• Forecast business outcomes with probabilistic models and confidence levels.
• Leverage Generative AI to improve workflows and efficiency.
• Design and analyze A/B tests and multivariate experiments (MVT).
• Map user journeys with GA4/Looker and translate findings for executives.

Skills

Data science

Statistical modeling

A/B testing

Communication

Adaptability

Education

Bachelor’s or Master’s in Data Science/Statistics/Math/Economics

Tools

SQL

Python

GA4

Looker

Excel

Prophet

ARIMA

Job description

We are looking for a Full Stack Data Science and Analytics professional to join our team. This is a hybrid role designed to bridge the gap between deep statistical modeling and agile product growth. The ideal candidate will handle the end-to-end data lifecycle: from maintaining robust data ingestion/quality to building sophisticated predictive models and leading product experimentation (A/B testing) to drive user engagement and business efficiency.

Your ‘day to day’:
1. Data Science & Modeling
● Predictive Modeling: Create predictive models, statistical reporting, and data analysis methodologies to identify trends in large, complex datasets.
● Cross-Functional Application: Apply analysis to various areas of the business, including but not limited to Market Economics, Supply Chain, Marketing/Advertising, and Scientific Research.
● Forecasting: Use predictive and prescriptive analytics tools to forecast business outcomes using probabilities and defined confidence levels.
● Innovation: Maintain up-to-date knowledge of existing and emerging scientific principles, theories, and techniques to identify and develop innovative solutions and projects.
● GenAI Integration: Leverage the latest developments in Generative AI technologies to improve efficiency in company business processes and automate manual workflows.
2. Product Analytics & Experimentation
● Experimentation Lifecycle: Design, execute, and analyze A/B tests and multivariate experiments (MVT). This includes hypothesis generation, sample size calculation, and determining statistical significance.
● User Behavior Insights: Utilize Product Analytics Tools such as Google Analytics (GA4) and Looker to map user journeys, identify drop-off points, and recommend features that increase product "stickiness."
● Strategy & Storytelling: Translate complex statistical findings into actionable insights for high-level stakeholders, including senior leadership and the CEO.

Who we’re looking for:

• Education: Bachelor’s or Master’s degree in Data Science, Statistics, Mathematics, Economics, or a related quantitative field.
• Professional Experience: Minimum of 5 years in Data Science, Product Analytics, or a closely related quantitative field.● Product Mindset: A strong "product-first" lens—the ability to ask why users behave a certainway, not just what the data says.
• Technical Capabilities: Advanced SQL (CTEs, Window Functions) and Python (Pandas, Scikit-learn, Statsmodels) is a must
• Expert-level proficiency in Product Analytics Tools Ex: Google Analytics (GA4) and Looker.
• Deep understanding of A/B Testing and experimental design (Frequentist or Bayesian) is required
• Advanced Excel (Financial modeling, complex formulas, and data manipulation) is a must
• Experience with time-series analysis (e.g., Prophet, ARIMA) and growth modeling (S-Curves) is a must
• Communication: Exceptional ability to simplify complex technical concepts for non-technical executive audiences.
• Adaptability: Comfort moving between long-term research projects and fast-paced experimentation cycles.