--- title: "Data Science and Analytics Services | Azendo" description: "Data scientists and analysts assigned to your questions. Statistical analysis, experimentation and reporting under one committed monthly agreement." url: "https://azendo.co/services/data-science/" --- [← All services](https://azendo.co/services/) # Data science services tied to a decision. Statistical analysis, experimentation and reporting, assigned against real business questions rather than delivered as a standing report nobody reads. Analysis is only as good as the data underneath it, so where pipelines and models are the actual constraint, [managed data services](https://azendo.co/services/data-engineering/) is the place to start. [Contact us](https://azendo.co/services/data-science/#team-builder) [Talk to a service delivery manager](https://azendo.co/get-in-touch/) How the role joins your team Added to delivery \+ capacity Data Scientist Assigned capacity * [Python](https://azendo.co/skills/python/) * [scikit-learn](https://azendo.co/skills/scikit-learn/) * [PyTorch](https://azendo.co/skills/pytorch/) Capacity 160 h committed monthly ## What is outsourced data science? Outsourced data science means contracting analysis and modelling capability externally: framing a question, testing it against available data, and delivering an answer a decision can be made on. It differs from data engineering, which builds and runs the pipelines, and from business intelligence, which reports on what already happened. ## What our data science team covers. The most useful assignments start from a decision someone needs to make rather than from a dataset someone wants explored. We will push for that framing at scoping, because it is what separates analysis people act on from analysis people file. Where the honest answer is that the data cannot support the question, we say so early. That is cheaper than a model built on a foundation nobody checked. * Model development From first hypothesis to a validated, production-ready model. * Fine-tuning and evaluation Models tuned and benchmarked against your real data, not generic datasets. * Data analysis Findings translated into a plan, not just a notebook. * Sprint planning and stand-ups In your rhythm, your tools, alongside your product owner. * Documentation and handover Methodology and evaluation results documented for the next iteration. ## When you do not need a model. Assignments usually cover a mix of recurring and ad hoc work. Experiment analysis and reporting on a cadence, alongside investigation of whatever question the business has this month. Both benefit from someone who already knows your data rather than starting cold each time. Not every question needs a model. A rules engine or a straightforward statistical approach answers a surprising share of what arrives framed as a machine learning problem, and recommending the simpler option is part of the job. Analysis is only as reliable as the pipelines underneath it. Where those are the constraint, data engineering is the place to start and we will say so before taking the work. Modelling * [Python](https://azendo.co/skills/python/) * [scikit-learn](https://azendo.co/skills/scikit-learn/) * [PyTorch](https://azendo.co/skills/pytorch/) * [XGBoost](https://azendo.co/skills/xgboost/) * [TensorFlow](https://azendo.co/skills/tensorflow/) * \+ more on request Analysis and notebooks * [Jupyter](https://azendo.co/skills/jupyter/) * [pandas](https://azendo.co/skills/pandas/) * [SQL](https://azendo.co/skills/sql/) * [R](https://azendo.co/skills/r/) * [Google Colab](https://azendo.co/skills/google-colab/) * \+ more on request Deployment handoff * [MLflow](https://azendo.co/skills/mlflow/) * [Docker](https://azendo.co/skills/docker/) * [MLOps handoff](https://azendo.co/skills/mlops-handoff/) * [FastAPI](https://azendo.co/skills/fastapi/) * [Kubeflow](https://azendo.co/skills/kubeflow/) * \+ more on request ## One fixed fee for a monthly average. We calculate the specialist's working hours across a full year, deduct annual leave and public holidays, then divide by twelve. That average becomes the fixed monthly capacity and price in your agreement, so budgeting for a data scientist stays predictable whether a given month runs light or heavy on hours. Where the question turns out to need a model in production rather than an analysis, that work moves to MLOps engineering under the same agreement rather than becoming a separate procurement, and model-backed product features sit with [AI engineering services](https://azendo.co/services/ai-engineering/). The arithmetic behind the fee is set out in [how the monthly fee is built](https://azendo.co/pricing/). 160 h Typical monthly capacity for one data scientist Fixed Monthly price, unaffected by leave or holidays 4–6 weeks From signed scope to delivery starting Monthly Cycle to raise or lower committed hours Analysis reviewed internally before it reaches your decision makers. Bangkok, Thailand — five hours ahead of Northern Europe ## The data science roles we assign. Take one role, or several as one delivery team. Each role below has its own page describing what it delivers under a service agreement. [Data Scientist Analysis, modelling and findings Read about the role →](https://azendo.co/services/data-science/data-scientist/)[Data Analyst Reporting your team can act on Read about the role →](https://azendo.co/services/data-science/data-analyst/)[BI Developer Semantic models and dashboards Read about the role →](https://azendo.co/services/data-science/bi-developer/)[Quantitative Analyst Pricing, risk and forecasting Read about the role →](https://azendo.co/services/data-science/quantitative-analyst/) ## Building in-house, a local agency, or Azendo. Each fits a different situation. An in-house role makes sense when the work is permanent and local; a local agency suits a one-off project with a clear end date. Azendo sits between the two — ongoing capacity for work that keeps coming, with the team, the workplace and the administration behind it handled on our side. | Comparison | Building it in-house | Local agency | Azendo | | ------------------------------- | ---------------------------------- | ----------------------------------------- | ----------------------------------------- | | Time to productive output | Months — recruit, onboard, ramp up | Fast to start, slow to learn your product | 4–6 weeks | | Continuity of context | Resets when someone leaves | Rebuilt with each new project | Held by the same delivery team, for years | | Continuity of product knowledge | Lost when the hire leaves | Ends with the project | Held by the assigned team | | Who answers for delivery | You do | Account manager, between projects | A service delivery manager, continuously | | Cost profile | Fixed, whatever the workload | Priced per project | One monthly fee, adjustable each cycle | | Scaling a discipline | A new hire each time | Re-scoped each engagement | Capacity up or down at the monthly cycle | ## Questions about our data science team. Can I choose the seniority level? Yes. Junior through principal data scientists are available, priced by level. We'll recommend a level based on the scope you share before delivery starts. What if the stack isn't listed above? Tell us what you use. The list above is what we see most often, not a limit — we'll confirm fit for your exact setup at scoping. Can I add a second data scientist later? Yes, at the next monthly cycle. Capacity moves with your roadmap rather than locking you into the original scope. Who owns the models and code the data scientist writes? You do. Work happens in your repositories, under your license terms, from the first commit. ## Often assigned alongside a data scientist. A specialist rarely works alone on a roadmap. These disciplines cover the ground around the role and can be added to the same service agreement. [AI engineering LLM features, retrieval and evaluation](https://azendo.co/services/ai-engineering/)[Data engineering Pipelines, warehouses and reporting models](https://azendo.co/services/data-engineering/)[MLOps engineering Deploying and monitoring models in production](https://azendo.co/services/mlops-engineering/)[AI automation engineering Agentic workflows and internal automation](https://azendo.co/services/ai-automation-engineering/) ## Data science services assigned to a decision The most useful assignments start from something someone has to decide, not from a dataset someone wants explored. We push for that framing at scoping because it separates analysis people act on from analysis people file. Not every question needs a model. A rules engine or a straightforward statistical approach answers a surprising share of what arrives framed as machine learning, and recommending the simpler option is part of the work. Analysis is only as reliable as the pipelines underneath it. Where those are the constraint, data engineering outsourcing comes first and we will say so before taking the work. Where a piece of analysis turns into a model that needs to run continuously, that work moves to MLOps under the same agreement rather than becoming a separate procurement. Capacity moves between disciplines at the monthly cycle. ## Tell us what data science capacity your roadmap needs. Tell us about your project and the capacity you have in mind. A service delivery manager will get back to you.