Senior Data Scientist — AI/ML & Causal Inference, Equity

Google LLC; New York (NY); Presencial; USD 174.000 - 252.000; Tempo integral

Resumo da vaga

Google is seeking a Senior Research Data Scientist to advance analytics, ML, and causal inference for Search Health. You will develop methodologies to identify product opportunities, frame ambiguous business problems, and communicate data-driven recommendations to senior leadership.

Responsibilities include leading end-to-end analyses, collaborating across product and engineering teams, and applying LLM-based approaches to improve health-related AI features.

Qualificações

• Master's degree or higher in a quantitative field.
• 5 years of work experience using analytics to solve product or business problems, coding (Python, R, SQL), querying databases or statistical analysis, or 3 years with a PhD.
• Experience with ML algorithms and statistical modeling.
• Experience with statistical data analysis, experimental design (A/B testing), and causal inference.
• Experience applying causal inference techniques to estimate treatment effects or perform counterfactual analysis.

Responsabilidades

• Conduct advanced analyses and causal inference to identify product opportunities and headroom for AI features.
• Frame and solve ambiguous business problems and communicate data-driven solutions to stakeholders.
• Lead projects autonomously, focusing on user impact and responsible growth.
• Collaborate cross-functionally with product, engineering, and leadership teams.

Conhecimentos

Python

R

SQL

ML algorithms

Statistical modeling

A/B testing

Causal inference

Experiment design

Communication

Formação

Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field

Ferramentas

Large Language Models (LLMs)

Descrição da vaga

Google is seeking a Senior Research Data Scientist to advance analytics, ML, and causal inference for Search Health. You will develop methodologies to identify product opportunities, frame ambiguous business problems, and communicate data-driven recommendations to senior leadership.

Responsibilities include leading end-to-end analyses, collaborating across product and engineering teams, and applying LLM-based approaches to improve health-related AI features.