Hire a data scientist from Netofficials to turn your data into forecasts, analytics and machine learning features your product can ship. We shape the role around your data, systems and roadmap. The senior engineer who scopes the work leads it, and you talk to them directly. Send your brief to start.
Your data scientist takes a forecasting, scoring, recommendation or anomaly-detection problem from raw data to a model your engineers can run in production. We frame the business question first and agree how the model will be evaluated before any training starts. Experiments stay tied to a decision, which cuts the rework that follows models that look strong in a notebook and never reach release.
Reliable models depend on well-structured data. If your data is scattered across tools or hard to query, your data scientist can scope data warehouse consulting next to the modelling work: source mapping, schema design, pipelines and reporting tables. You see what must be fixed before modelling and what can wait. If a separate data warehousing project would serve you better, we will recommend it after scoping.
Your data scientist works inside a cross-functional Netofficials team with back-end, QA and DevOps engineers, so models are tested, deployed and documented instead of living as one person's scripts. At release you receive documentation and deployment access, and you own the source code and IP, so your team can retrain, monitor and extend the work without relying on us.
You describe the data, the decision or model you need and the systems it must connect to, such as forecasting, analytics, a machine learning feature or data warehouse consulting work. We turn that into a role brief and a proposed data scientist fit.
You receiveYou confirm the role and the data problem it covers
You check the data scientist's technical background against your stack and data. We agree the role expectations, how work is reviewed and what the data scientist owns alongside your engineers.
You receiveYou approve the proposed data scientist before commitment
We plan access to your data sources, notebooks, pipelines and repositories, then set the communication window and meeting schedule with you. The contract confirms IP assignment before any work begins.
You receiveYou sign off the onboarding plan before work starts
The data scientist begins on a shared backlog with your team and our technical lead, who also scoped the work. You review results at each sprint end while model and data work move toward production beside your engineers.
You receiveYou review results at each sprint end
Send us your product, the modelling or data warehouse skills you need and your preferred working hours. The senior engineer who scopes the role replies with a proposed fit, a technical review and next steps.
A full-time remote squad built around your product, suited to continuing data science work such as forecasting, machine learning features and data warehouse development.
One data scientist joins your existing engineering or analytics team, which suits you if your data platform and delivery process are already in place.
A fixed-price project with a defined outcome, such as a first predictive model or a data warehouse build, for work you can scope in advance.
Cost is quoted after we review the role and your scope, so we don't publish a fixed rate. Three things drive the quote: how much data science work you need, how many data sources and systems it connects to, and whether you choose a dedicated team, one engineer through staff augmentation or a fixed-price project. Scope, assumptions and review milestones are agreed in writing before work starts. Send a brief with your data, goal and working hours, and we will reply with a proposed fit and the basis for a quote.
You do. IP assignment is confirmed in the contract before the build begins, so the models, source code and pipeline work belong to you from the start. We hand over documentation and deployment access at release. If your data is sensitive, we can agree a mutual NDA before you share any details.
Yes, when the data science work depends on it. Forecasting, analytics and machine learning features need reliable, well-modelled data, so data warehouse consulting is often a sensible first scope. Netofficials runs one cross-functional team covering back-end, QA and DevOps around the data work. If another route would serve you better after scoping, such as building the warehouse before any modelling, we will say so and recommend it.
Tell us what the product does, which data you hold, the modelling or warehouse skills you need and your working hours. The senior engineer who scopes your brief also leads the work, so you talk straight to the technical lead, with no account manager in between. We reply with a proposed fit, and you review the role expectations and technical approach before committing. If a data warehouse build or a fixed-price project would suit you better than a hire, we will recommend it after scoping.
