A data scientist uses statistics, machine learning and programming to answer business questions with data: finding patterns, building predictive models, designing experiments and explaining the results so managers can make better decisions.
Hire data scientists to uncover patterns, build predictive models and quantify business impact. Our data scientists combine statistics, machine learning and domain understanding to deliver forecasting, segmentation, churn and pricing models.
Every one of our data scientists is technically vetted, communicates clearly in English and works with sufficient time-zone overlap for daily collaboration. Choose full-time, part-time or hourly engagement, start quickly and scale your team as your roadmap evolves.
Skills our data scientists bring to your team from day one.
Share your requirements, review hand-picked profiles, interview the candidates you like and onboard within days. Your developer works as part of your team, using your tools and processes, while we take care of HR, infrastructure, security and replacements.
Engagement models: Full-time dedicated (around 160 hours per month), part-time (around 80 hours per month) and hourly or project-based arrangements.
Our data scientists work in Python (pandas, scikit-learn, statsmodels, XGBoost, Prophet) and SQL, sometimes R, using Jupyter, Databricks and Azure Machine Learning. They explain models with SHAP, visualise results in Power BI or Plotly, and work with ML engineers to deploy models on Azure or AWS.
Billing is monthly, and you can add people or reduce hours with reasonable notice as your needs change. Data scientists pair well with data engineers and ML developers. For ongoing analysis as a service, see data insights as a service.
Ready to hire data scientists? Tell us about your data and the questions you need answered, and we will share suitable profiles. Share your requirements.
Common questions about hiring data scientists through Prominent AI Services LLP.
Analysts focus on reporting and descriptive insight, while data scientists build predictive and prescriptive models.
Yes, often together with an ML engineer, using APIs and MLOps practices for production use.
Yes. You can add resources or reduce hours with reasonable notice as your project needs change.
Rates depend on experience, specialisation and engagement model. We share rates with each profile so you can choose the right fit for your budget.
Our data scientists typically have degrees in statistics, mathematics, computer science or engineering, and practical experience building models for business use.
Yes. Cleaning and understanding data is part of the job. They will assess quality first and tell you honestly what the data can support.
Not necessarily. A data scientist can start with exports and existing databases. If larger-scale work is needed, they can recommend a data platform or work with a data engineer.
