Data Scientist

Responsibilities

• Provide analytical input and explain methods clearly to internal teams.
• Guide and support the development of proposals by providing methodological ideas and data science recommendations.
• Drive the integration of new tools, including GenAI, synthetic data models, and automation solutions, to meet client needs.
• Work closely with specialized data science teams across multiple countries to provide excellent service in a fast‑paced environment.
• Lead the execution of day‑to‑day requests by writing clean, well‑structured Python code to prepare data and run models.
• Support the setup, execution, and quality control of analytical pipelines, checking data and outputs thoroughly.
• Develop working knowledge of GenAI tools, APIs (e.g., LiteLLM, OpenAI, Langchain), and LLM‑based workflows.
• Create analytical deliverables such as code, outputs, and documentation while continuously seeking improvements.
• Produce accurate, well‑structured analytical outputs and write commentary to provide recommendations.
• Prepare, plan, and prioritize tasks effectively using tools like Jira to ensure deliverables are sent to schedule.
• Apply Ipsos’ quality, safety, and MRS Code of Conduct principles in all project work.

Requirements

• Degree in Data Science, Statistics/Mathematics, Computer Science, Actuarial Science, or related quantitative studies.
• Running and executing code within JupyterLab environments and managing virtual machine instances.
• Training and deploying models using GPUs accessed through Google Cloud Platform (GCP) services like Vertex AI.
• Fine‑tuning GenAI models/LLMs for specific use cases and implementing RAG.
• Using Git and GitHub to manage code changes and collaborate with others.
• Proficiency in Python, R, SQL, and Google Cloud Platform is essential.
• Understanding of coding principles and design patterns to write clean code.
• Knowledge of designing agent architectures to optimize the use of GenAI models/LLMs.