01
TRCI-26-06865
Classic AI/ML Data Scientist
This contract role focuses on building, validating, and deploying classical predictive and statistical machine-learning models that inform business decisions and provide signals to an AI ecosystem. The core requirements are 4+ years of production experience, strong Python and SQL, a solid foundation in statistics, experimental design, feature engineering and model evaluation, and experience with cloud data environments and MLOps tools. Relevant modeling work includes forecasting, classification, regression, clustering, anomaly detection, propensity scoring and NLP; PyTorch and TensorFlow are included among the requested Python tools. The experience band is 4–6 years, and the stated location is Europe; work mode is not specified. The engagement is contract, and no specific industry or product domain is stated.
Position summary
- Location
- Europe
- Workplace
- Hybrid
- Employment
- Contract
- Experience
- Minimum 4 years and Maximum 6 years
Role overview
Why This Role Matters.
This contract role focuses on building, validating, and deploying classical predictive and statistical machine-learning models that inform business decisions and provide signals to an AI ecosystem. The core requirements are 4+ years of production experience, strong Python and SQL, a solid foundation in statistics, experimental design, feature engineering and model evaluation, and experience with cloud data environments and MLOps tools. Relevant modeling work includes forecasting, classification, regression, clustering, anomaly detection, propensity scoring and NLP; PyTorch and TensorFlow are included among the requested Python tools. The experience band is 4–6 years, and the stated location is Europe; work mode is not specified. The engagement is contract, and no specific industry or product domain is stated.
Your Impact
Deliver Enterprise Value
Help organisations solve complex business problems through modern technology, consulting expertise and measurable outcomes.
Collaboration
Work Across Teams
Collaborate with consultants, architects, engineers and client stakeholders throughout the project lifecycle.
Growth
Learn Continuously
Gain exposure to enterprise technologies, certifications, mentoring and real-world project experience.
Career Path
Grow With Ubique
Build a long-term consulting career with opportunities to take on greater responsibility and leadership over time.
Responsibilities
What You'll Be Doing.
Every role at Ubique contributes directly to solving meaningful business challenges for our clients.
02
Develop, train, and validate predictive models (gradient boosting, time-series forecasting, regression, clustering, NLP).
03
Build reproducible feature engineering and model training pipelines in Python and SQL.
04
Collaborate with Data Engineers to deploy models into production with automated monitoring for data drift and model degradation.
05
Communicate model insights, confidence intervals, and business impact clearly to non-technical stakeholders.
Technology stack
Tools & Technologies.
The platforms and technologies you'll use to build modern, enterprise-grade solutions.
01
Python
Required
02
pandas
Required
03
scikit-learn
Required
04
sklearn
Required
05
XGBoost
Required
06
LightGBM
Required
07
PyTorch
Required
08
TensorFlow
Required
09
statsmodels
Required
10
SQL
Required
11
Databricks
Required
12
Snowflake
Required
13
AWS
Required
14
Azure
Required
15
MLflow
Required
16
Git
Required
17
MLOps
Required
18
A/B testing
Required
19
time-series forecasting
Required
20
gradient boosting
Required
Requirements
Skills & Experience.
We value curiosity, collaboration and continuous learning. If you don't meet every requirement but believe you can make an impact, we'd still love to hear from you.
Essential
Required Qualifications
4+ years building and deploying classical machine-learning and statistical models in production
Python for data science and machine learning, including pandas and scikit-learn
SQL and analysis of structured datasets
Statistics, experimental design including A/B testing, and model evaluation metrics
Feature engineering and reproducible model training pipelines
Experience with a cloud data environment such as Databricks, Snowflake, AWS, or Azure
MLOps and version-control tooling, including MLflow and Git
Model deployment and automated monitoring for data drift and model degradation
Preferred
Nice to Have
Gradient boosting with XGBoost or LightGBM
Time-series forecasting
Classification, regression, clustering, anomaly detection, propensity scoring, or NLP modeling
PyTorch or TensorFlow
Exploratory data analysis on large-scale structured datasets
Communicating model insights, confidence intervals, and business impact to non-technical stakeholders
Pragmatic selection of simple, reliable models
What you'll gain
More Than Just A Job.
We're committed to helping every team member grow professionally, personally and technically while working on meaningful projects.
Global Exposure
Collaborate with international clients and multicultural teams on enterprise programmes.
Continuous Learning
Expand your expertise through mentoring, certifications and hands-on project experience.
Career Growth
Take ownership, develop leadership skills and grow your consulting career over time.
Flexible Working
Hybrid and remote collaboration designed around trust and delivering exceptional outcomes.
People First
Join a supportive culture where collaboration, respect and long-term relationships come first.
Enterprise Projects
Work on meaningful technology initiatives for leading organisations across industries.
Apply
Apply for Classic AI/ML Data Scientist
One page, about two minutes. We only ask for what we actually need to have a first conversation.