Hire data scientists who work with your real data to build models, forecasts and AI features, and who take them beyond the notebook into production.
Python ecosystemModels in productionThe languages, libraries and platforms our data scientists use, and what each one is for.
Analysis and modelling in Python, with notebooks for exploration and clean code for production.
Cleaning, joining and exploring data to find what is actually useful before modelling.
Classification, regression, clustering and forecasting models chosen for the problem, not the trend.
Neural networks for images, text and other unstructured data when simpler models fall short.
Retrieval, prompt design and evaluation for LLM features built on your own data.
Processing large datasets with distributed tools when a single machine isn’t enough.
Packaging models as services with monitoring, so they keep working as data changes.
Data science pays off when it answers a specific business question. These are typical projects our data scientists work on.
Demand, sales and capacity forecasts that feed planning decisions.
Product and content recommendations based on behaviour and similarity.
Models that categorise documents, flag risk or score leads.
Assistants, search and summarisation grounded in your own documents and data.
Exploratory analysis and dashboards that explain what is happening and why.
Image and video models for inspection, recognition and document processing.
See finished projects in our case studies.
Levels describe the scope of responsibility you can expect. The profiles we put forward depend on your requirements.
Analyses data and builds models for well-defined problems.
Teams with a senior data lead that need more analysis and modelling capacity.
Owns problems from question to production model.
Products that need someone to own a model or an AI feature end to end.
Sets the direction for data science and ML.
Companies starting or scaling a data science or AI function.
From your first message to a specialist working in your team.
Proposed workflow Steps shown for review. Final process to be confirmed.
Hiring through a development partner gives you more than a CV. You get specialists who work as part of your team, with a company behind them.
We shortlist data scientists by the tools and kind of product you work on, not by job title alone.
You review profiles and interview candidates yourself before an engagement starts.
They join your repositories, stand-ups and tools, and follow your team’s processes.
Start with one specialist and add people or adjust the setup as your plans change.
360Xpert also provides developers, designers, QA and DevOps engineers if your project needs them later.
Access to the data relevant to the problem and someone who understands it. If the data isn’t ready, the first step is usually assessing and preparing it.
Yes. They work with your engineers, or ours, to package models as services and monitor them after release.
Yes, including retrieval over your documents, prompt design and evaluating output quality.
Yes. You review profiles and interview shortlisted candidates in your own format, and you decide who joins your team.
From a focused proof of concept to ongoing work. We agree the duration with you at the start and it can be extended.
Yes. You can add one data scientist, a team that includes data and ML engineers, or hand us the whole project.
Tell us about your data and the problem you want to solve. We’ll get back to you to discuss suitable data scientists and next steps.