Senior ML Engineer: Customer Analytics & Recommendations

Ubique Systems; Helsinki; On-site; EUR 85,000 - 120,000; Full time

Job summary

Ubique Systems in Helsinki is seeking an experienced ML Engineer to develop, train, validate, and deploy models for churn, propensity, LTV, and recommendations. You will collaborate with data engineering and business teams to build scalable feature stores and predictive pipelines using modern MLOps.

The role emphasizes end-to-end model development, feature engineering, time-based validation, and deployment within a lakehouse architecture on AWS (SageMaker, MLflow, Kubeflow).

Qualifications

• Experience building ML models for churn, propensity, and lifetime value.
• Experience with feature stores and predictive pipelines.
• Collaborating with data engineers and business teams to translate requirements into models.
• Proficiency with MLOps tools and cloud platforms.

Responsibilities

• Develop machine learning models for churn, upsell/cross-sell propensity, LTV, and next-best-action recommendations.
• Translate business definitions into model targets, eligibility populations, and prediction horizons.
• Define target/label logic for churn, revenue contraction, acquisition, renewal, and customer value use cases.
• Collaborate with data engineers to build training and scoring datasets from governed feature stores.
• Perform exploratory analysis, feature selection, leakage assessment, and time-based validation.
• Develop sentiment-based features from customer text and assess their impact on models.
• Generate and rank next-best-action recommendations using model scores and business priorities.
• Validate model performance across time periods and customer segments.

Skills

ML/AI concepts

Feature engineering

Model validation

Time-based validation

Sentiment analysis

Tools

SQL

PySpark

Python

AWS S3

Kubeflow

MLflow

SageMaker

Dremio

Iceberg/Delta

Job description

Ubique Systems in Helsinki is seeking an experienced ML Engineer to develop, train, validate, and deploy models for churn, propensity, LTV, and recommendations. You will collaborate with data engineering and business teams to build scalable feature stores and predictive pipelines using modern MLOps.

The role emphasizes end-to-end model development, feature engineering, time-based validation, and deployment within a lakehouse architecture on AWS (SageMaker, MLflow, Kubeflow).