The table of content
About the Project
We are looking for a Machine Learning Engineer to work on a demand forecasting and personalisation platform for a subscription-based digital product. The team builds models for churn prediction, user segmentation, recommendation logic, demand trends, and operational forecasting.
The technical environment includes Python, PyTorch or scikit-learn depending on the use case, Pandas, SQL, Airflow, MLflow, Docker, and cloud-based model deployment. The role requires regular communication with international data, product, and engineering teams, so strong spoken English is essential.
What You Will Do
- Develop and improve ML models for churn prediction, segmentation, forecasting, recommendations, and anomaly detection.
- Work with structured behavioural, transactional, and product usage data.
- Create reproducible training pipelines, feature engineering workflows, and model evaluation reports.
- Collaborate with data engineers on reliable datasets, feature quality, and pipeline monitoring.
- Deploy models as batch jobs or API-based services depending on product requirements.
- Track model performance, drift, quality metrics, and business impact after release.
- Communicate model assumptions, limitations, and results to product and business stakeholders.
What We Are Looking For
- 3+ years of commercial experience in machine learning or applied data science.
- Strong Python skills and practical experience with Pandas, NumPy, scikit-learn, and/or PyTorch.
- Good understanding of supervised learning, model validation, metrics, feature engineering, and data leakage risks.
- Experience with SQL and working with production datasets.
- Experience with MLflow, Airflow, Docker, or similar tools for reproducible ML workflows.
- Ability to move beyond notebooks and contribute to production-ready ML pipelines.
- Strong spoken English - B2+ or higher for explaining model behaviour and technical trade-offs to international teams.
Nice to Have
- Experience with time-series forecasting, ranking, recommendation systems, or uplift modelling.
- Experience with cloud platforms such as AWS, GCP, or Azure.
- Experience with feature stores, model monitoring, or A/B testing frameworks.
- Background in subscription products, e-commerce, fintech, adtech, or marketplace analytics.
Apply
If you enjoy building ML systems that connect modelling quality with real product outcomes, we would be glad to hear from you. Send us your CV and we will contact you to discuss relevant opportunities.
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