Senior Data Scientist- Real Estate

Senior Data Scientist - Real Estate JOB SUMMARY: As a member of the Enterprise Data & Analytics Team, the Data Scientist is an everyday partner with multiple departments, including Global Real Estate, Global Marketing, Category Management, Shared Services & Finance, Global Technology, and Business Unit Leadership. This is a unique opportunity to work on a new, growing team within a large organization. The Senior Data Scientist is responsible for delivering advanced analytics projects that drive business results. The ideal candidate should possess the ability to independently deliver all aspects of project work, including interpreting business questions and desired outcomes, selecting the appropriate methodology, data cleaning, exploratory data analysis, model building, and creation of polished deliverables that are suitable for internal and external presentation. We are looking for an individual who is a proven problem solver with exceptional critical thinking ability. The candidate should have a high sense of curiosity and be comfortable with ambiguity when faced with a difficult challenge. Additionally, the candidate should possess the abilities to collaborate with others and simply and effectively communicate complex concepts with a non-technical audience. RESPONSIBILITIES/ACCOUNTABILITIES: Independently develop advanced analytics and predictive models from design through implementation in the areas of real estate site selection forecasts and network optimization. Additional knowledge in areas including pricing and promotion, marketing, and merchandising would also be preferred. Review data science models, code refactoring, packaging, versioning and monitoring of model output quality. Design, improve, document, develop, code, and test sophisticated analytical prototype models in a big-data environment. Utilize software development tools and methodologies such as Git, CI/CD, and Agile. Use statistical-econometric, machine learning methods to design and suggest experiments for data driven merchandising and new ways to establish causality and answer strategic questions using data. Measure the overall business value of the models and concisely explain complex analytical findings to non-analytical peers and business leaders. Design and implement end-to-end data pipelines: work closely with Data engineering to build data assets (schemas, tables, views) that support business processes. Ideate, architect, build and enhance the platform for our Merch Pricing Engine. Drive strategic initiative and vision for various project data needs using ETL and data management architecture to support predictive models, analytical models, and niche data processing use cases such as geo spatial transformations. KNOWLEDGE, SKILLS AND OTHER QUALIFICATIONS REQUIRED: Experience in the design, development, deployment and monitoring of large-scale production machine learning systems. Demonstrated knowledge of SQL, R, and Python. Experience querying large datasets using Hive/SparkSQL/PySpark. Experience with ML frameworks, including a combination of the following: scikit-learn, Tensorflow, Keras, Pytorch, SparkMLlib, etc. Experience with software development practices, object-oriented principles, and test automation. Experience with version control systems, including Git or Mercurial. Experience with cloud-based analytics environments including Azure, AWS, or GCP. Experience applying operational research, statistical, and machine learning techniques, including a combination of the following: classification, regression, time series forecasting, clustering, optimization, anomaly detection, etc. Experienced with agile methodologies using project planning and tracking management tools, including JIRA or Rally. Strong problem-solving skills and ability to troubleshoot complex distributed systems. Strong interpersonal skills, including the ability to communicate the business benefits of analytics. Ability to design, develop, and implement advanced statistical models, machine learning algorithms, and predictive analytics solutions using large geospatial and transactional datasets. Knowledge of commercial real estate preferred. Availability to travel up to 10% of the time. EDUCATION/TRAINING REQUIRED: Bachelor’s or Master’s degree required with a quantitative focus (Statistics, Business Analytics, Data Science, Math, Economics, etc.). Master’s degree with 3+ years experience or Bachelor’s degree with 4+ years of experience in a data science/advanced analytics role. Experience with Geospatial analytics strongly preferred.

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