Data Scientist (GMU MSU)

Ipsos is one of the world’s largest research companies and currently the only one primarily managed by researchers, ranking as a #1 full-service research organization for four consecutive years. With over 75 different data-driven solutions, and presence in 90 markets, Ipsos brings together research, implementation, methodological, and subject-matter experts from around the world, combining thematic and technical experts to deliver top-quality research and insights. Simply speaking, we help the biggest companies solve some of their biggest problems, serving more than 5000 clients across the globe by providing research, data, and insights on their target markets. And we are proud of our continuous efforts in making Ipsos the best place to work!

JOB PURPOSE

 

• 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.

 

 

ENVIRONMENT

 

Joining Ipsos means developing your career in the very top tier of research. With 16,600 people employed in 89 countries, we are one of the largest and best-known research companies. If you’re interested in keeping company with some of the most naturally curious researchers and making an impact to the best local and global brands, we invite you to join us.

As a curious and intuitive individual, an enthusiastic professional with a passion for creating knowledge, at Ipsos you will be fully dedicated to finding the best solutions for our clients. At Ipsos we foster an environment that is entrepreneurial and forward thinking. We attract and hire innovative people with inquiring minds who possess drive, intelligence and enthusiasm. Working at Ipsos is dynamic and challenging, never a dull moment!

 

OUTPUTS/ACCOUNTABILITY

 

• 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.

 

 

RELATIONSHIPS

 

• Act as a focal point for internal teams, advising on simpler analytics and helping them understand how to apply different techniques.
• Work collaboratively with specialized data science teams and data engineers in a highly skilled and supportive environment.
• Communicate clearly with internal teams and explain analytical concepts in simple terms.
• Promote teamwork across the business area and behave in a manner that displays professionalism and integrity.

 

 

DEVELOPMENT

 

• Proactively learn new techniques, tools, and technologies, particularly those related to GenAI and cloud-based analytics.
• Take ownership of personal development objectives as part of growth within the data science pathway.
• Share learnings from projects within the team and contribute to internal training and knowledge-sharing sessions.

 

 

BACKGROUND

 

• Experience:
  • 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.

 

• Skills/Knowledge:
  • Technical Tools: Proficiency in Python, R, SQL, and Google Cloud Platform is essential.
  • Analytical Techniques: Understanding of coding principles and design patterns to write clean code.
  • AI/ML: Knowledge of designing agent architectures to optimize the use of GenAI models/LLMs.

 

• Qualifications:
  • Degree in Data Science, Statistics/Mathematics, Computer Science, Actuarial Science, or related quantitative studies.