Data Scientist

JLL supports the Whole You, personally and professionally.

Our people at JLL are shaping the future of real estate for a better world by combining world class services, advisory and technology to our clients. We are committed to hiring the best, most talented people in our industry; and we support them through professional growth, flexibility, and personalized benefits to manage life in and outside of work.  Whether you’ve got deep experience in commercial real estate, skilled trades, and technology, or you’re looking to apply your relevant experience to a new industry, we empower you to shape a brighter way forward so you can thrive professionally and personally.

The objective of this role is to extract business value from JLL and external datasets, infuse intelligence into JLLT technology products, and help drive superior business outcomes for our organization and our clients.

The job entails a wide range of data-related activities starting with the extraction and preparation of data and ending with the validation and presentation of results or with the deployment of an intelligence product. The Data Scientist is expected to pull data from source systems; explore, validate, clean, and transform the data; leverage a wide range of algorithms and analytical approaches including Business Intelligence (BI), Machine Learning (ML), Natural Language Processing (NLP) and Artificial Intelligence (AI); deliver intelligence with full reproducibility; test and deploy the intelligence products following MLOps and DataOps best practices; and communicate results and insights to key stakeholders.

The data scientist will consult with product owners, subject matter experts, and other stakeholders to understand the business questions or objectives. Then work in cross-functional teams to identify the best approach and to deliver the solution.

Key Responsibilities:

• Deliver high-quality, action-oriented, compelling insights and recommendations for decision makers based on descriptive, predictive, and prescriptive data analytics.
• Deliver models and complex data analyses using rigorous statistical methodologies, machine learning algorithms, and AI techniques.
• Research new algorithms and libraries and deliver innovative solutions using the best available tool for the job.
• Consult with internal or external clients, partners, and stakeholders.  Understand business needs and challenges. Advice on BI/AI strategies applied to Commercial Real Estate.
• Help groom requirements and generate benefit/cost estimates for BI/AI solutions.
• Deliver high-impact BI solutions including dashboards, visualizations, and extensions.

• Find, collect, explore, validate, and prepare the data for analysis. Responsibilities would include querying, scraping, loading, validating, cleaning, aggregating, transforming, detecting outliers, imputing missing values, developing features, and scripting these processes.

• Thoroughly validate quality of outputs and perform peer review for other team members.
• Collaborate with and provide support to the productizing and operating the intelligence.
• Apply DataOps, MLOps, and Engineering best practices. Automate the extraction, preparation, and analysis of data to deliver reproducible results. Automate the training, validation, deployment, and monitoring of models.  Develop reusable code and reusable intelligence services. Follow CI/CD development processes and architectural guidelines.
• Manage projects, ongoing requests, and resolve issues to ensure delivery on time and according to specifications.

Required Skills:

• Excellent quantitative, analytical, and problem-solving skills.
• Ability to use data to answer business questions, find new insights, understand drivers, detect potential issues, and make recommendations to decision makers.
• Knowledgeable about descriptive and inferential statistics.
• Knowledgeable about machine learning algorithms including implementing classification models, regression models, cluster analysis.
• Knowledgeable about deep learning frameworks such as TensorFlow or PyTorch.
• Knowledgeable about prompt engineering, Retrieval-Augmented Generation (RAG) and LLM evaluation techniques.
• Ability to implement processes to extract, load, and transform data from different types of structured and unstructured sources.
• Knowledgeable about feature-engineering and development of data pipelines.
• Ability to develop impactful visualizations, dashboards, and reports.
• Team player able to collaborate effectively in large cross-functional projects.
• Client orientation and business acumen required to drive impact.
• High attention to detail and quality.
• Solid written and verbal communication skills.
• Ability to multi-task, prioritize, and execut