Franklin Templeton - Data Scientist

About the Research & Strategy Department

The Research & Strategy team delivers quantitative insights that guide Clarion's global real estate investment decisions. The team includes researchers, data scientists, and analysts who collaborate to build models, analyze markets, and provide actionable intelligence to portfolio and investment teams. You will join a group that values analytical rigor, experimentation, and continuous learning-offering strong opportunities to deepen your expertise in applied data science within a real estate investment environment.

How You Will Add Value

Core Responsibilities

- You will support research that informs commercial real estate investment decisions.

- You will develop and test statistical and machine learning models.

- You will analyze structured and semi structured datasets.

- You will maintain forecasting and ranking models across markets.

- You will evaluate model performance and update models when required.

- You will apply model results to scenarios, risk analysis, and portfolio insights.

- You will contribute to experiments exploring new features and modeling approaches.

- You will maintain shared codebases and data pipelines.

- You will collaborate with global teams to communicate insights.

- You will document methods and results clearly.

What Will Help You Be Successful

Experience, Education & Certifications

- Degree in Mathematics, Statistics, Computer Science, or related discipline.

- 2-4 years of experience in data science or quantitative research.

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

- Experience maintaining forecasting models in production.

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

- Experience with linear and tree based machine learning models.

- Working knowledge of SQL and data engineering fundamentals.

Technical Skills

- Experience taking research models into production.

- Hands on exposure to full analytical pipelines.

- Knowledge of risk modeling or portfolio optimization.

- Experience with generative AI and natural language processing techniques.

- Familiarity with MLOps practices.

- Experience with alternative or unstructured datasets.

- Proficiency with Azure, Snowflake, and orchestration tools like Airflow or Data Factory.

Soft Skills

- Strong communication skills for technical and non technical audiences.

- Ability to manage data science project work and stakeholder needs.