Senior Data Scientist

InterContinental Hotels Group; Philippines; On-site; PHP 1,500,000 - 2,300,000; Full time

Job summary

InterContinental Hotels Group is seeking a senior data scientist to lead the development of predictive and prescriptive models across marketing, revenue, and operations. You will design experiments, build uplift and LTV models, and scale solutions in cloud environments.

Collaborate with business leaders to identify high-value opportunities, define governance, and contribute to reusable frameworks. Strong analytical skills and ability to translate insights into business impact are essential.

Qualifications

• Bachelor’s or Master’s in a quantitative field or equivalent experience.
• 5–8 years of data science with business impact.
• Strong SQL and Python; ML/LLM frameworks such as scikit-learn, XGBoost, TensorFlow, PyTorch.
• Cloud deployment expertise (GCP Vertex AI, BigQueryML; AWS SageMaker).
• Experience in causal inference, optimization algorithms, and experimental design.
• Expertise in model governance and performance monitoring.
• Strong business acumen and ability to translate insights into ROI/LTV.
• Independent problem solver with strong critical thinking.

Responsibilities

• Lead development of high-impact predictive and prescriptive models across multiple domains (advanced segmentation, personalization, pricing, forecasting).
• Design and oversee complex experiments, including A/B and multivariate tests.
• Drive uplift modeling and long-term value prediction to optimize targeting and offers.
• Research/adapt methods for hard problems; lead PoCs; scale viable solutions.
• Partner with business leaders to identify high-value opportunities for predictive model deployment.
• Co-design MLOps (feature store, experimentation, monitoring) with DE/PT; ensure interoperability & governance.
• Apply causal inference techniques for marketing and product measurement.
• Publish technical best practices and reusable frameworks.

Skills

SQL

Python

Statistical modeling

Experiment design

Business acumen

Critical thinking

Independent problem solving

LTV/ROI modelling

Education

Bachelor’s or Master’s in a quantitative field

Tools

scikit-learn

XGBoost

TensorFlow

PyTorch

Vertex AI

SageMaker

BigQueryML

Job description

Key Accountabilities

• Lead development of high-impact predictive and prescriptive models across multiple domains (advanced segmentation, personalization, pricing, forecasting, advanced measurement)
• Design and oversee complex experiments, including A/B and multivariate and tests
• Drive uplift modeling and long-term value prediction to optimize targeting and offers
• Research/adapt methods for hard problems; lead PoCs; scale viable solutions
• Partner with business leaders to identify high-value opportunities for predictive model deployment
• Co-design MLOps (feature store, experimentation, monitoring) with DE/P&T; ensure interoperability & governance
• Apply causal inference techniques for marketing and product measurement.
• Publish technical best practices and reusable frameworks

Education

Bachelor’s or Master’s in a quantitative field (ex: mathematics, statistics, data science) or an equivalent combination of education and work related experience

Experience

5-8 years of relevant work experience in Data Science with business-facing impact delivery

Technical Skills and Knowledge

• Strong SQL (advanced queries, stored procs, performance tuning) and Python programming skills
• Advanced SQL, Python, and ML and LLM frameworks (scikit-learn, XGBoost, TensorFlow, PyTorch, Gemini)
• Proficiency deploying models in cloud environments (GCP - BigQueryML & Vertex AI, AWS SageMaker)
• Proficiency in causal inference, optimization algorithms, and advanced experiment design
• Expert in GCP/Vertex AI and MLOps orchestration tools
• Advanced model governance and performance monitoring skills
• Strong grasp of business impact modeling (ROI, LTV, incrementality)
• Independent problem solver with strong critical thinking skills and business acumen
• Strong statistical modeling (logistic/linear regression, time series, propensity modeling)
• Experimental design (A/B, multivariate)
• Feature engineering best practices