Service · Software development

Hire a data science specialist from a permanent team.

A data scientist who does not come in as a loose freelancer and then leave again, but works as part of a team. With fellow modellers, MLOps engineers and data engineers behind them. Code that is transferable, experiments that are reproducible, and replaceability when needed.

Arrange an introductory call See our work

A data scientist from our team, not a loose freelancer.

The usual way to quickly bring in data science capacity is to call a freelancer or staffing agency. That works, until the person leaves after a few months and nobody knows exactly how the notebook worked, which parameters the production model version used, or where the training data lives. You are left with code that nobody takes responsibility for.

We supply a data scientist on request, but as part of a team. Behind the specialist who works alongside you day to day stands a group of colleagues with overlapping expertise: other data scientists for discussing model choices, MLOps engineers for production deployment, data engineers for pipelines, and AI engineers for LLM work. If someone falls ill or leaves, the work does not stall, because code, experiments and context are shared within the team.

You are not hiring a lone individual, you are hiring a specialist with backup. Code review by a senior colleague is standard. Experiments run under version control with MLflow or DVC, so that someone else can reproduce the results. Dutch time zone, meetings in Dutch if preferred, and a contract your legal department can normally work with.

If you are specifically looking for a data scientist in the Netherlands, that is exactly what we focus on. No offshore arrangement where you have to coordinate across a different time zone, no marketplace where the person is with another client in six weeks and you have to start over. A team in Amsterdam, with colleagues who know each other and review each other's work.

What a data scientist from us brings as standard.

Team backup

Colleague data scientist, MLOps and data engineer as backup

Reproducible

MLflow, DVC and notebook version control as standard

Code review

Every pull request reviewed by a senior colleague

Replaceable

If someone falls ill or leaves, the work carries on

Ways you can engage a data scientist.

Depending on whether you need a single person or a complete project. We do not offer junior-only staffing; we provide mid-level, senior or lead-level engineers only.

Option 01

Dedicated data scientist

One specialist, full-time on your project

A data scientist who works entirely on your challenge: exploratory data analysis, model development, validation and deployment. Works from your stack, in your repository, with your stakeholders. Behind the scenes, sounding board for colleagues on architecture choices and code review. Suited to organisations with a defined project (predictive model, segmentation, forecasting) or an ongoing need for data science capacity without wanting to build an in-house team.

Mid-level, senior or leadCode review includedDutch time zoneIn your stack

Way of working 02

Team augmentation

Reinforcement alongside your existing data team

You already have a data team but are missing a specific skill, such as an experienced NLP specialist, a computer vision expert or someone with deep causal inference knowledge. Our data scientist joins your squad, works in your tools and cadence, and hands over carefully. Short or longer-term engagement. Often combined with our Python developers or a Databricks specialist where the stack requires it.

Squad fitFilling a skills gapShort or long-term engagementKnowledge sharing

Way of working 03

Project-based engagement with a team

Full scope delivery with multiple roles

For projects too large for one person, such as an end-to-end demand forecasting platform, a fraud detection system with production deployment, or an ML platform foundation. We deliver a team: data scientist(s), MLOps engineer, data engineer and project lead. Clearly defined scope, joint architecture decisions with your side, and an ongoing maintenance phase. Often combined with our enterprise AI implementation projects.

Multidisciplinary teamEnd-to-endProduction and maintenanceEnd-to-end accountability

What a data scientist with us actually does.

A data scientist is neither a business analyst nor an ML engineer. Rather, someone who approaches your data in an exploratory, statistical and experimental way.

Exploratory analysis

First look at customer data, hypothesis formation, basic statistics, data quality report.

Predictive modelling

Customer churn, demand forecasting, fraud detection, condition prognosis, lead scoring.

Time series and clustering

Sales, inventory or energy forecasting, customer segmentation, anomaly detection.

NLP and computer vision

Sentiment, topic extraction, document search, defect detection, OCR, quality inspection.

Experiments and causal analysis

A/B test analysis, multi-armed bandits, attribution, causal inference on campaigns.

MLOps and recommendations

Model monitoring, drift detection, retraining pipelines, cross-sell and upsell engines.

When you need a data scientist (and when someone else).

Not every data question calls for a data scientist. Often a business analyst, ML engineer or AI engineer is the better choice. Below is the distinction we apply.

Data scientist

Experimental modelling and inference

When you do not yet know what works: formulating hypotheses, comparing models, statistical validation, building prototypes. Customer churn models, demand forecasting, segmentation, NLP or computer vision research. We provide mid-level, senior and lead engineers.

Business analyst

BI, dashboards and SQL questions

When you need operational figures and dashboards: revenue by region, conversion funnels, KPI reporting. No modelling, but strong SQL, a firm grasp of business questions and clear visualisation. Sometimes a KPI dashboard project is all you need.

ML engineer

Production setup and performance tuning

When a model has proven itself and needs to go into production: serving infrastructure, latency tuning, model monitoring, retraining pipelines. Our data scientists work alongside ML engineers within the team to move from notebook to production.

AI engineer

LLM applications, RAG and prompt engineering

When you want to build GenAI applications: retrieval-augmented generation, fine-tuning of language models, agent architectures. A separate role that overlaps with data science but requires a different skill set. We keep these roles separate, so tell us what you are looking for.

How a placement process works.

01Introduction→ 02Match→ 03Start→ 04Ongoing

First conversation

Introduction

Which question, which data, which skill level, which working model fits. No obligation.

Profiles & conversation

Match proposal

We propose one or two profiles, with CV and context. You meet them and choose.

Onboarding week

Start within your team

Access to data and repository, introductions to stakeholders, initial working agreements.

Sprints & review

Ongoing involvement

Fortnightly cadence, code review by a senior colleague, sparring within the team.

Why a data science agency works differently from ad hoc contracting.

The data scientists you hire through a freelance marketplace or secondment are often excellent people. The problem lies not with them but with the structure. A freelancer works alone. There is no colleague to discuss a model choice with, no one reviewing the notebook, and when they leave, the knowledge leaves with them. For a short prototype that is fine. For production applications the risk is considerable.

We operate differently as a data science agency. The specialist you hire through us sits within a permanent Dutch team. Code, experiments and context are shared through structured repositories, MLflow experiment tracking and DVC data version control. Pull requests are reviewed by senior colleagues before they go to production. For longer engagements we often also make an MLOps engineer and a data engineer available as part-time back-up, so that production deployment and data pipelines do not rest on the data scientist alone.

For related services in the same chain, from data pipeline construction to the dashboard layer to platform choice, we work together as one team. Also read data integration consulting if your question starts with data pipelines, or Databricks specialists if you are building on that platform. The common thread is that you are not hiring loose individuals, but working with an organisation that takes responsibility for the entire process.

In practice, this means we can take on different types of assignments without having to recruit a new individual each time. A short exploratory analysis over a few sprints? A mid-level data scientist with a senior as sounding board. A serious production deployment of a fraud detection model? A senior data scientist, an MLOps engineer and a lead for architecture. Ongoing involvement alongside your own team for a year or longer? One fixed person with a second-in-line who reviews the work and can step in. The engagement model adapts to the need, not the other way around.

The stack our data scientists work in.

For each assignment we choose what fits within your existing stack. No dogma about a single framework, but deep experience with the common Python, SQL and cloud tooling.

Modelling & languages

PythonpandasNumPyscikit-learnPyTorchTensorFlowstatsmodelsR

Data & warehouse

PostgreSQLClickHouseSnowflakeBigQueryDatabricksJupyterHexDeepnote

MLOps & cloud

MLflowWeights & BiasesDVCVertex AISageMakerAWS / GCP / AzurePlotly / Streamlit

Frequently asked questions about hiring a data scientist.

What is the difference between a data scientist, a business analyst and an ML engineer?

A data scientist works experimentally: formulating hypotheses, comparing models and using statistics to establish what works and what doesn't. Predictive models, segmentation, causal analysis. A business analyst works at BI and SQL level: dashboards, KPI reporting, conversion analysis without modelling. An ML engineer picks up where the data scientist finishes, getting a proven model into production, with serving infrastructure, monitoring and retraining. An AI engineer focuses specifically on LLM applications, RAG and prompt engineering. We keep these roles separate within the team, so we can match you with the right person for the right question rather than delivering one generic "data person".

When is the right moment to hire a data scientist?

There are three typical moments. One: you have a specific problem where standard BI falls short, for example predicting which of your customers will churn, or which price is optimal. Two: you are building a data product (recommendation engine, fraud detection, demand forecasting) where modelling is central to the proposition. Three: you want to make an existing process evidence-based, such as A/B test analysis, attribution questions, or causal inference on marketing spend. It is too early when you do not yet have a data foundation; in that case you start with data engineering or a dashboard layer, and the data scientist comes later. We will give you an honest view of which phase is relevant for you.

Do your data scientists work entirely in the Netherlands?

Yes. Our data scientists work in the Dutch time zone, hold meetings in Dutch or English (depending on your team), and are physically present where that suits. This is where we specifically differ from offshore agencies that make hourly rates look attractive but create communication overhead and time-zone friction. For "hire data scientist Netherlands" searches, this is precisely the proposition.

What if the data scientist you hire falls ill or leaves?

That is the core difference from loose freelance hiring. With us, every data scientist works within a team where code, experiments and context are shared. In case of illness, colleagues pick up ongoing work or we provide a replacement with a handover. If someone leaves our company voluntarily, we ensure the handover to a successor, not you. MLflow history, DVC versions and code review logs make that handover practically workable.

Is MLOps and production deployment included?

In the "project-based engagement with a team" format, it is included as standard, with an MLOps engineer alongside the data scientist. With "dedicated data scientist" or "team augmentation", it depends on what is agreed: the data scientist can handle basic MLOps themselves (MLflow, containers, simple serving), or we add a part-time MLOps engineer for production deployment, drift detection and retraining pipelines. We discuss this at the outset so you are not caught off guard once the model needs to leave the notebook.

Do you also do LLM, RAG or GenAI work?

Yes, but through a separate role: the AI engineer. There is overlap with data science (both work with Python, models and data), but the skill set differs fundamentally. LLM engineering revolves around prompt engineering, retrieval architectures, evaluation frameworks and fine-tuning, whereas classical data science revolves around statistics, feature engineering and model selection. If your project calls for both, we deploy both roles. For a broader AI strategy, see our page on enterprise AI implementation.

Under which contract types can we hire a data scientist?

This is usually arranged on a secondment or consultancy basis, with an agreed weekly commitment (for example four or five days) and a notice period that suits a secondment. No ZZP pass-through routes or dubious structures: you contract directly with Appfront. For longer engagements, we can also agree a fixed-price scope for a defined delivery phase, followed by hourly resourcing for further development. Which format fits depends on your procurement preferences and the nature of the work.

What does it cost to hire a data scientist?

The rate depends on seniority level (mid-level, senior or lead), the time commitment (part-time or full-time), the duration, and whether additional roles (MLOps, data engineer) join the team. We don't publish "from" prices on the website, because the range between a short mid-level engagement and a full team led by a lead data scientist is too wide to communicate honestly. After the introductory call, we provide a substantiated rate and a scope proposal that your procurement team can work with.

Talk to us about your data science challenge.

A no-obligation conversation of around half an hour. We listen to the question (which data, what goal, which stage) and decide together whether a data scientist is the answer or whether another role fits better. Including if the outcome is that you're better off with another provider.

Schedule an introductory call Email fabian.vandijk@appfront.nl