Senior-level data science solutions with full deployment — per hour

Advanced projects carry complexity and vary widely. Discussing your business goals, data, technical limits and expected outcome ensures the work can be scoped accurately. This protects both parties by giving you clear deliverables and a precise estimate while confirming a good fit prior to any payment.

Each package represents a set block of senior engineering hours. Choose the one closest to your project size, then adjust the quantity at checkout if additional time is required — for instance, buying the smallest block three times handles a larger task. Final deliverables and scope are agreed upon before work starts.

You will be notified well before the allocated hours are exhausted. You can then choose to either extend the project scope or reprioritise the tasks within the already agreed hours. Nothing is charged past what you explicitly approve.

Both options involve hands-on engineering, building models, writing code and deploying systems. The distinction lies in depth: this senior tier handles multi-component system design, advanced architectures, full continuous-integration pipeline creation and production deployment complete with documentation. The standard option fits a focused, well-defined task such as preparing data, training a single model, or a simple deployment. Reach out via message if you need help deciding which is appropriate.

Yes. You must provide the data source or dataset. During the scoping phase, you will receive guidance regarding expected volume, quality, format and any required preparation prior to development.

Deployments take place primarily on Hugging Face and Google Cloud Platform, using continuous-integration pipelines built with Docker, MLflow and GitHub Actions to keep the system maintainable, scalable and simple to update post-handover.

Yes. All trained models, source code, documentation and pipeline configurations belong to you, enabling your own team to independently maintain, extend and retrain the system after project completion.

Yes. Taking over projects that have stalled, hit technical roadblocks or require senior expertise to reach production is frequent. Share whatever is currently available — notebooks, code, documentation — during scoping, and development will proceed from that point rather than starting anew.

Standard setups feature automated retraining triggers, containerised deployment via Docker, continuous integration through GitHub Actions, model versioning and experiment tracking with MLflow, along with deployment scripts for Hugging Face or Google Cloud Platform. The exact configuration is determined during scoping based on the project.

Yes, every package includes documentation tailored to the delivered work. Larger packages feature comprehensive written records detailing the system architecture, data pipeline, model performance, deployment process and maintenance instructions — providing enough detail for your internal team or another developer to manage the system moving forward.

No. Every package involves hands-on engineering. Even a single-hour block constitutes active development time — writing code, evaluating models, building pipeline parts, debugging or resolving technical issues. It represents a genuine hour of senior work addressing a small task, a quick fix, or advancing a portion of a larger project.