Senior Data Scientist (ML)

York Towers is the development arm of the York Holding Group. Through continuous research and the ability to predict emerging trends, the company keeps an edge over the market. Playing a leading role in driving the successful development and diversification of one of the most vital economic sectors, York Towers is committed to creating residential products across the country that provide residents with distinctive, universal, multicultural, and enriching lifestyles. York Towers is the exclusive developer of luxury real estate worldwide. The company aims to become a leading real estate player through an efficient business model and advanced technologies used for designing and construction. Since its establishment in 2016, York Towers has delivered 20 real estate properties and runs six dynamic projects. York Towers operates eight offices in five countries across three continents.

Machine Learning Engineer, Data Intelligence

About the Role

We are seeking a Machine Learning Engineer to join our Data Intelligence team and help transform the real estate landscape. You will own predictive models end to end, from feature engineering through production and ongoing monitoring, and help extract market insights that power investment decisions and platform intelligence. You will also support and mentor junior ML engineers on the team.

Responsibilities

- Build and productionise machine learning models for property price prediction across sale and rent, spanning apartments, villas, land, and floor units, as well as district-level price forecasting.

- Own feature engineering on diverse real estate datasets, including temporal and rolling-window features that capture market trends.

- Drive model selection and hyperparameter tuning to improve accuracy, primarily using gradient-boosted methods such as XGBoost and CatBoost.

- Monitor models in production for data and concept drift, and design and operate automated retraining pipelines, including data validation and model version control.

- Evaluate model performance using MAPE, MdAPE, MAE, and error-band distribution analysis, and interpret model behaviour using tools such as SHAP.

- Collaborate with data engineers to deploy and serve models reliably, and mentor junior ML engineers through code review, model reviews, and technical guidance.

Required Skills

- Strong Python skills with scikit-learn, pandas, and NumPy.

- Hands-on experience with gradient-boosting frameworks (XGBoost, CatBoost or LightGBM) on tabular data.

- Solid feature engineering and model evaluation fundamentals, with practical experience in hyperparameter optimisation.

- Experience productionising models and monitoring them in service, including drift detection (e.g. Evidently) and automated retraining.

- Experience containerising applications with Docker for reproducible training and deployment.

- Working knowledge of SQL and PostgreSQL, and comfort on AWS-hosted infrastructure.

- Experience with real estate or financial datasets is a plus.

Experience Level

- 4+ years in data science or applied machine learning, with a track record of shipping models to production.

- Experience mentoring or guiding junior engineers is preferred.

- Prior work in real estate, fintech, or a similar domain is a plus.