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Principal Data Scientist fulltime/parttime (hybrid)

Reads like a ML Engineer role: putting models into production.

Pay
Pay not listedSimilar roles state $166K–$240K
Work setting
Not stated
Employment
Not stated

What you’ll work on

Full posting
  • Own end-to-end the SmartRisk Engine, the group-wide core rating and probability-of-default model

  • Design, train, calibrate, validate, deploy and improve machine learning models

  • Monitor model performance and run drift analyses across markets and customer segments

  • Build and enhance scalable end-to-end ML pipelines with testing, versioning and deployment

  • Set and drive MLOps standards for CI/CD, monitoring, alerting and incident management

From the employer’s posting
You take end-to-end ownership of the SmartRisk Engine
You design, train, calibrate, validate, deploy, and continuously improve machine learning models
You monitor model performance, conduct drift analyses across markets and customer segments
You build and enhance scalable end-to-end ML pipelines, including testing, versioning, documentation, and production deployment
You establish and drive best-practice MLOps standards, including CI/CD, monitoring, alerting, model registries, data quality controls, and incident management

Posting record

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Status in our records
Live
First seen by us
Oct 16, 202619 days ago
Recorded sightings
19
Last seen by us
Nov 4, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Pay

$166K–$240K is typical

The middle half of what similar US postings state. This one doesn’t list pay.

From 1,279 postings in the last year. Stated ranges, not salaries paid.

Time since we first saw it

Day 19

Similar jobs usually come down between day 6 and day 69.

From 4,347 similar postings that came down. Coming down is not always a hire.

DKV Mobility is hiring

3 open jobs

Open jobs by the week we first saw them, last 12 weeks.

Hiring record

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About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025
$140,300
Expected change, 2025 to 2035
21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

View original posting ↗

Do you want to actively contribute to a company’s growth and help turn big plans into reality? Then join our team! You can expect exciting challenges, an inspiring environment, and a work culture that combines flexibility and collaboration – supported by our 60/40 hybrid model and modern offices in Ratingen as a vibrant hub for new ideas. As part of our team, you will take on the following responsibilities: You take end-to-end ownership of the SmartRisk Engine, the group-wide core rating and probability-of-default model that drives onboarding and offboarding decisions in Credit Management. You design, train, calibrate, validate, deploy, and continuously improve machine learning models to ensure high business impact and reliability. You monitor model performance, conduct drift analyses across markets and customer segments, and drive improvements to both models and risk strategies. You build and enhance scalable end-to-end ML pipelines, including testing, versioning, documentation, and production deployment. You establish and drive best-practice MLOps standards, including CI/CD, monitoring, alerting, model registries, data quality controls, and incident management. You act as a trusted advisor to management and business stakeholders while providing technical leadership, mentoring, and guidance to data scientists.
Source details

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Location & working pattern

Ratingen, NRW, DE

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