About the Role
We’re looking for a hands-on Data Scientist / ML Engineer to take ownership of an existing production machine learning system currently managed by external consultant. You will become the in-house expert responsible for maintaining, improving, and extending these models, while also developing new ML solutions across additional parts of the platform.
This is a high-ownership role requiring strong independence, deep analytical thinking, and the ability to translate complex business logic into scalable machine learning systems that directly impact core business performance.
Responsibilities
- Own and maintain existing production ML models built by external teams
- Collaborate with external consultants to understand and improve current model architecture
- Translate business logic into machine learning solutions and optimization problems
- Improve existing models through feature engineering, retraining, and tuning
- Design and build new ML models for additional system components
- Work with large-scale datasets (SQL + Python) to extract insights and build features
- Monitor model performance and implement retraining pipelines
Requirements
- B.Sc. in Computer Science, Mathematics, Statistics, Engineering, or related field (M.Sc. advantage)
- 2+ years of experience in machine learning / data science roles in production environments
- Strong Python skills (NumPy, Pandas, Scikit-learn; TensorFlow or PyTorch advantage)
- Strong SQL skills and experience working with large datasets
- Experience with supervised and unsupervised learning methods
- Knowledge of neural networks and ensemble methods (e.g., boosting, random forests)
- Experience building or improving production ML models
- Strong analytical and problem-solving skills
- Ability to work independently and take full ownership of systems
Nice to have:
- Experience with LLMs (prompt engineering, RAG, fine-tuning)
- Experience with sequential decision-making or optimization models
- Experience working with external ML vendors or consultants
