Data Scientist

Description :

- Develop, validate, and deploy statistical and machine learning models for forecasting, ranking, and risk analysis.

- Analyze structured and semi-structured datasets to derive actionable insights.

- Maintain and enhance forecasting and predictive models across multiple markets.

- Monitor model performance and implement improvements as required.

- Apply model outputs to scenario analysis, portfolio insights, and risk evaluation.

- Experiment with new features, datasets, and modeling approaches to improve model accuracy.

- Build and maintain scalable data pipelines and shared codebases.

- Collaborate with global stakeholders and cross-functional teams to communicate insights effectively.

- Document methodologies, processes, and findings clearly for business and technical audiences.

Required Qualifications & Experience :

- Bachelors or Masters degree in Mathematics, Statistics, Computer Science, or a related field.

- 2 to 4 years of experience in Data Science, Quantitative Research, or a related domain.

- Strong foundation in probability, statistics, machine learning, and time series analysis.

- Experience in developing and maintaining production-level forecasting models.

- Advanced proficiency in Python (pandas, NumPy, scikit-learn).

- Hands-on experience with linear models and tree-based algorithms.

- Working knowledge of SQL and data engineering fundamentals.

Preferred Skills :

- Prior experience in asset management, financial services, or real estate analytics.

- Exposure to risk modeling, portfolio optimization, or investment analytics.

- Experience working with alternative or unstructured datasets.

- Familiarity with Generative AI and Natural Language Processing (NLP) techniques.

- Experience in deploying models into production and managing end-to-end data science pipelines.

Soft Skills :

- Strong communication skills for technical and non technical audiences.

- Ability to manage data science project work and stakeholder needs