In-House Data Team vs SpiderHunts

The question is how much work, for how long

The comparison is usually framed as salary against agency day rate, and on that basis an employee almost always looks cheaper. That framing misses the real question: how much data work will you have, of what kind, over the next three years?

A single data scientist working on one churn model is expensive if the model takes three months and there is no second project. The same person is a bargain if there are twenty projects queued and the business wants to own that capability permanently. Our broader comparison of agency, freelancer and in-house options makes the same point for software generally. Data work sharpens it.

What a first data hire actually faces

A lone data scientist joining a business without a data team walks into a set of jobs that are rarely in the job description.

• Finding where the data lives, and who controls access to each system
• Cleaning and joining it, which is usually most of the first few months
• Building pipelines, APIs and deployment, which is engineering work many data scientists did not train for
• Explaining results to managers who expected an answer in weeks
• Monitoring and retraining the first model while being asked for the second

Some people thrive in that role. Many leave within eighteen months because they were hired to model and spent their time plumbing. When they leave, the model often leaves with them in practical terms, because nobody else knows how it was built.

An honest side-by-side

Neither column is better. They suit different situations, and pretending otherwise is how businesses end up with an idle team or a pile of unmaintained agency work.

When hiring in-house is clearly right

If your product is a data product, or machine learning is part of what customers pay for, you need people inside the business who think about it every day. The same is true when you expect a steady stream of analyses and models across departments for years, or when regulators expect named internal ownership of models that make decisions about people.

In those cases we would still argue for hiring an engineer with the first data scientist, not after. A model that cannot be deployed or maintained is a slide deck.

When a partner is the better use of money

A partner makes sense when you have a small number of well-defined projects, when you do not yet know whether ML will pay off for you, or when you need modelling and production engineering delivered together. SpiderHunts works on fixed scopes with the code in your repository from the first commit, so what you are buying is a working system rather than hours.

Hire for the work you are certain you will have. Rent capacity for the work you are still testing.

It also suits businesses who tried hiring and struggled to attract experienced people. That is common outside the largest cities, and there is no shame in it.

The hybrid most growing businesses land on

The arrangement we see working best in businesses of roughly 30 to 300 staff is one internal owner of data, often an analyst or an engineer with data interests, plus a partner for the builds. The internal person knows the business, sets priorities and maintains what is built. The partner delivers the larger projects and hands them over properly.

1. Appoint or hire one person who owns data questions internally
2. Bring in a partner for the first one or two production models, with that person embedded in the project
3. Hand over maintenance to the internal owner, with a light retainer as backup
4. Hire further only when the queue of work justifies a second salary

Our data science and machine learning teams are set up for exactly this, including working alongside a single internal analyst rather than around them.

Questions to settle before deciding

Write down the data projects you are confident about for the next two years, not the ones that would be nice. Estimate how often a model will need attention once live. Ask who in the business would own a model's decisions if it went wrong. The answers usually make the choice obvious, and they are useful whichever route you take.

Frequently asked questions