Machine learning
Hire machine learning engineers who watch the model after launch
A prediction, score or forecast is the easy part of a pilot. The hard part is knowing when the data underneath it has moved, and what to do about it before the model is quietly wrong.
What you are getting
4 things that decide this
- 01Engineers who start from your baseline and your target metric, not from a choice of architecture. If a simple rule beats a model on your data, they will tell you that first.
- 02Dataset shift and label leakage treated as first-class risks, checked before a model ships and watched for after it, not discovered when accuracy quietly drops.
- 03Models that go live behind monitoring, with a real threshold and an alert, not as a notebook someone reruns by hand once a month.
- 04You interview each engineer yourself. The training data, the evaluation code and the monitoring are yours from the first commit.
The role we actually hire for
Most ML pilots stall in the same place: the model works in a notebook and nobody trusts it in production. That gap is rarely the algorithm. It is the data pipeline feeding it, the metric nobody agreed on, and the absence of anything watching the model once it ships.
Our engineers start every engagement by writing down the baseline: what a simple rule or the current process already achieves. A model that beats a coin flip is not automatically worth deploying if a lookup table gets you 90% of the way there. The baseline is what makes the model's value measurable instead of assumed.
TankAware is a fair example of the shape of this work, built for petroleum site operator Sutherland Excavating. Predictive alerts on IoT tank-sensor data flag anomalies before they become failures, and the platform reported 30% higher inspection accuracy once the model was live. That number came from comparing against the manual process it replaced, which is the baseline discipline applied to a real deployment.
The judgment a senior ML hire brings
Starts from the metric
Precision, recall, and calibration are decided with you before training starts, not chosen afterward to make a number look good.
Checks for label leakage
A feature that accidentally encodes the answer makes a model look excellent in testing and fail the moment it meets new data.
Treats dataset shift as expected
Production data drifts from training data over time. The question is not whether it happens, but how fast you find out.
Ships behind monitoring
A deployed model gets a dashboard tracking its inputs and its accuracy against fresh labels, not a one-time evaluation report.
Keeps the pipeline reviewable
Feature code and training data live in your repository under version control, so a retrain is a diff someone can read.
- BaselineWhat the current process already gets right
- Data auditChecked for leakage before training starts
- TrainingScored against the metric you set, not accuracy alone
- EvaluationHeld-out data the model never saw
- DeploymentBehind a monitor, not a notebook
- Drift watchAlerts when production data stops matching training data
The last station is the one a pilot usually skips. A model with no drift watch degrades silently, and the first sign is a business metric moving for reasons nobody can trace.
Prediction and scoring systems in production
How hiring works
- 01
Describe the prediction problem
A free call about what you are trying to score, forecast or classify, and what data you already have to work with.
- 02
Meet the engineers
We shortlist engineers who have taken a model from a notebook into a monitored production system, and you interview them your own way.
- 03
They embed
Your codebase, your repository, your deploy process. One engineer owns the pipeline and the evaluation, and answers for both.
- 04
They hand over
A documented baseline, the evaluation results, and the drift alerts wired to your team. Where a client wants the model watched after launch, we stay on under a service level we agree. The first 2 months of support and maintenance are free, with every build.
Stack
Modeling
Production layer
Practices
Bring us the prediction problem behind the pilot
Show us what you are trying to score, forecast or classify. The scoping call is free, and you will leave knowing what a real baseline for your problem looks like.
01What is the difference between a machine learning engineer and a data scientist?
A data scientist typically explores data and builds a model that answers a question once. A machine learning engineer takes that model, or builds one directly, and makes it run reliably in production. That means the pipeline that feeds it, the service that serves predictions, and the monitoring that watches it after launch. Our engineers do both. A model nobody can deploy has not solved the problem.
02How do you decide if a problem even needs machine learning?
By comparing against the baseline first. A set of rules, a lookup table or the current manual process often gets close to what a model would deliver. It usually costs far less to maintain, too. We build the model when the baseline genuinely falls short, and say so plainly when it does not.
03Why does dataset shift matter after a model has already launched?
Dataset shift is when the data a model meets in production stops matching the data it was trained on. Accuracy drops even though the model itself never changed. It is common when user behavior, sensor conditions or market patterns move over time. Monitoring catches it early, before the drop shows up as a business problem nobody can explain.
04Do you work with the data we already have, or do we need a data warehouse first?
Most engagements start with the data you already have, wherever it lives: a production database, spreadsheets, sensor logs. We build the pipeline to get it into a trainable shape. A warehouse becomes worth building once the data volume or the number of models justifies it, not before.
05How do you handle label leakage and other common failure modes?
By auditing features before training, not after a model performs suspiciously well. Leakage happens when a feature accidentally encodes information the model would not have at prediction time. It is one of the most common reasons a model looks excellent in testing and then fails against real, new data.
06What drives the cost of an ML engagement?
How much of the work is data preparation versus modeling. Also whether the model needs to run behind a monitored production service or as a periodic batch job. Scoping calls are free. Where we must work inside an existing codebase or data pipeline, a paid two-week diagnostic ends with a fixed price.
Read next
- Machine learning development →How we scope and build a model from your operational data.
- Model engineering →Taking a model from evaluation into a served, monitored system.
- Dataset shift, defined →Why a model can degrade with no code change at all.
- Hire data engineers →The pipeline work a model depends on, on its own.
