Data Science Consultant

The Data Science Consultant works on several client projects in parallel, from raw data through to the final presentation. Day to day, this means:

• Collecting, cleaning, and preparing data of varying quality, mainly with Python and Power Query.
• Building reports, dashboards, and automations in Power BI and the Microsoft Power Platform.
• Developing predictive and machine learning models based on what each client needs.
• Designing and building AI agent pipelines that automate processes in production.
• Owning the full deliverable, including the visualizations and the presentation of results.
• Explaining scope, limitations, and results clearly to both technical and business audiences.
• Contributing to proposals, requirements gathering, and scoping of new projects.
• Documenting the work so that anyone on the team can reproduce it.

Success in the role looks like clients coming back for a second project, and deliverables that keep running after we hand them over.

Qualifications and requirements

• A degree in Computer Science, Engineering, Economics, Mathematics, or another quantitative field.
• 2+ years of experience in data science, analytics, or a related area.
• Demonstrable experience with Power BI, Power Query, and the Power Platform, with at least two projects where you built the solution end to end: source connections, data transformation, and reports in production.
• Experience designing AI agent pipelines that reached production.
• Working knowledge of Python for data science, including pandas, NumPy, and scikit-learn. This is assessed during the technical test.
• SQL, plus familiarity with cloud data platforms such as Databricks, Snowflake, BigQuery, Azure, AWS, or GCP.
• Experience working with messy or low quality data, including preprocessing and statistical analysis.
• Advanced professional English. This one is a hard requirement, since most client work is in English.
• The ability to organize a workload, deliver on time with minimal supervision, and give early warning when timelines are at risk.
• Honesty about what a model can and cannot do. We would rather hear the limitation early than read an overstated result.