Data Science

Models that reach production - and stay there.

Senior ML engineers who wrap forecasting and models in pipelines, monitoring, and automated retraining - so the notebook that worked in the demo keeps working in production.

The brief we usually get

Two problems we are called in to fix.

Concrete starting points, each with the team we field, the stack, and what lands in the first 30 days.

Forecasting in production

Typical problemA promising forecasting notebook that nobody can run on a schedule or trust over time.

What we shipA retraining pipeline with drift detection, an eval gate, and monitoring you can read.

Example stackPythonMLflowDatabricksAzure

First 30 daysOne model in production with automated retraining and monitoring wired end-to-end.

MLOps foundations

Typical problemEvery model is a one-off; no experiment tracking, no reproducibility, no path to prod.

What we shipA model registry, CI for models, and a promotion flow from staging to production.

Example stackMLflowPrefectFeature storeDocker

First 30 daysTracked experiments and a repeatable train-eval-register flow behind a gate.

Deliverables

What you get.

Clear model outputs your business can evaluate and your technical team can operate.

01 / FrameBusiness metric and baseline

02 / ValidateTracked evaluation gate

03 / OperateMonitored model release

Scope

Model brief

Business objective, target metric, input data, baseline, and acceptance criteria in one readable document.

Why it mattersEveryone agrees what success means before modeling starts.

Build

Production model package

A trained model with versioning, ownership notes, and the deployment path your team approves.

Why it mattersThe work moves beyond a notebook and into an operating service.

Control

Monitoring plan

Drift, quality, and performance checks written in business language with clear escalation rules.

Why it mattersYou know when the model is still trustworthy and when it needs attention.

Handover

Handover documentation

Retraining cadence, model assumptions, known limitations, and maintenance guidance.

Why it mattersYour team can operate the model without reverse-engineering our work.

Stack we work in

From notebook to a monitored service.

Modeling

Pythonscikit-learnPyTorchXGBoost

Forecasting

ProphetstatsmodelsLightGBMNumPyro

MLOps

MLflowPrefectFeature storeDVC

Compute

DatabricksSparkRaySageMaker

Serving

FastAPIBentoMLTritonBatch

Monitoring

EvidentlyWhyLabsGrafana

How we work

From question to monitored model.

We keep the model work tied to one decision, one metric, and one operating path your team can own.

PHASE 01

Framing

Define the decision the model informs and the metric that means success.

Time1 week

DeliverableProblem + eval spec

PHASE 02

Baseline & data

A simple baseline and a clean feature pipeline before any fancy model.

Time1-2 weeks

DeliverableBaseline + features

PHASE 03

Productionize

Pipeline, retraining, drift and eval gates, and serving behind an API or batch job.

TimeOngoing

DeliverableModel in production

PHASE 04

Operate & hand off

Monitoring dashboards, model card, and runbooks. Your team owns it.

TimeMonth 4+

DeliverableOwnership handover

Who delivers this

Named engineers from the bench

A snapshot. You interview the actual people before anyone joins your team.

LuanML engineer

Available Sep

ML Engineer

8 yrs · forecasting & MLOps

PythonPyTorchMLflowDatabricks

EnglishC2

GermanA2

InterviewsCleared · 1 client

EliraData scientist

Available now

Senior Data Scientist

7 yrs · demand & pricing

PythonLightGBMProphetSQL

EnglishC1

GermanB1

InterviewsReady to interview

Request a shortlist

Send us the ML role. We'll return a named shortlist.

Tell us the problem and the stack. Within 5 business days you get 2-4 named engineers with CVs and rates from 290 - 380€/day, to interview yourself.

Request developers See how the model works

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