We are looking for a Data Scientist to join our Data practice and develop statistical and machine learning solutions for enterprise clients across audience and customer analytics, personalization, measurement, and forecasting. You will also develop and evaluate LLM-based and agentic applications, ranging from single-agent tools to multi-step workflows. Working closely with Data Engineers, Strategists, and Designers, you will help transition models and AI agents from analysis into production environments.

Key Responsibilities

• Translate client business questions into analytical and modeling problems, selecting appropriate approaches and techniques to address them.

• Develop, validate, and deploy predictive models for use cases such as propensity modeling, segmentation, churn prediction, forecasting, and recommendations.

• Design and analyze experiments, including A/B testing and incrementality studies, and communicate findings to both technical and non-technical audiences.

• Design and develop agentic applications, including tool-enabled agents, multi-step workflows, retrieval-augmented generation (RAG), and prompt design.

• Develop evaluation frameworks for AI systems, covering test sets, quality metrics, guardrails, and regression checks to assess model and agent behavior.

• Leverage AI agents and automated workflows to accelerate analysis, code development, and documentation while validating their outputs.

• Collaborate with Data Engineers to bring features, models, and agents into production using Google Cloud Platform (BigQuery, Vertex AI), AWS (SageMaker, Bedrock), and Databricks (Spark, MLflow).

• Document methodologies, assumptions, and limitations, and present findings to clients and internal leadership.

Requirements

• At least 1 year of professional Data Science experience delivering models or analyses that inform business decisions. Internships, academic projects, competition entries, and bootcamp work do not count toward this requirement.

• Proficiency in Python, including pandas and scikit-learn, along with at least one of PyTorch, TensorFlow, or XGBoost, and strong SQL skills.

• Solid knowledge of statistics, experimental design, and model evaluation.

• Experience taking at least one model or analytical product from development into a production or client-facing environment.

• Hands-on experience developing LLM applications, including prompting, embeddings, retrieval, and an agent framework or API-based orchestration.

• Experience with at least one cloud-based ML environment, such as GCP (BigQuery, Vertex AI), AWS (SageMaker, Bedrock), or Databricks (Spark, MLflow). Experience across multiple platforms is an advantage.

• Ability to clearly communicate technical findings and results to business stakeholders.

• A Bachelor's degree in a quantitative discipline, such as Statistics, Computer Science, Mathematics, Engineering, or a related field, or equivalent professional experience. An advanced degree is a plus.

• Advanced English proficiency to support collaboration with global teams.