Predictive Analytics Specialist (ID#5549)

New Value Solutions (NVS) is seeking an experienced Predictive Analytics Specialist to support a leading real estate organization based in Downtown Calgary.

The Predictive Analytics Specialist will design, develop, deploy, and support advanced analytics solutions that help drive business decisions across a large real estate portfolio. The successful candidate will bring strong hands-on expertise in machine learning and predictive analytics, along with experience in data science, statistical modelling, forecasting, data engineering, and business intelligence.

Responsibilities

• Design, develop, and maintain predictive analytics and machine-learning models.


• Develop models focused on resident renewal/churn, delinquency risk, occupancy/vacancy, leasing pipelines, rental revenue, net operating income (NOI), budgeting, property maintenance, and supply-chain management.


• Apply machine learning and statistical modelling to identify trends, patterns, risks, and business opportunities.


• Develop forecasting models and measure and improve model accuracy and business impact.


• Monitor model performance, data drift, prediction accuracy, and user adoption.


• Transform property-management, leasing, financial, and operational data into actionable predictive insights.


• Partner with business teams to identify opportunities where analytics and artificial intelligence can deliver measurable value.


• Translate complex analytical findings into clear business recommendations.


• Build and maintain enterprise data pipelines using Azure Data Factory (ADF).


• Develop scalable SQL Server database structures, data models, and semantic models.


• Build analytical dashboards and reports using Power BI.


• Write SQL queries and DAX calculations to support data analysis and reporting.


• Support data quality, cleansing, and governance activities.


• Perform QA and coordinate solution UAT with business stakeholders.


• Collaborate with IT and business teams throughout solution design, development, deployment, and ongoing support.


• Work independently under general direction while taking ownership of assigned analytics initiatives.

Requirements

• Minimum 3+ years of experience in data science, machine learning, predictive analytics, advanced analytics, or a related area.


• Strong hands-on experience with machine learning and predictive modelling.


• Demonstrated experience delivering predictive analytics, statistical analysis, data-mining, or data-science solutions.


• Strong Python programming experience.


• Experience working with large and complex datasets.


• Experience developing forecasting and predictive models and evaluating model accuracy.


• Experience with SQL and data modelling.


• Experience with technologies such as Pandas, NumPy, Scikit-Learn, XGBoost, TensorFlow, or PyTorch.


• Experience with Azure Data Factory, Power BI, Azure Data Lake, SQL Server, or related Microsoft data technologies.


• Strong analytical, problem-solving, and attention-to-detail skills.


• Strong communication skills with the ability to translate technical findings into business recommendations.


• Ability to work effectively both independently and within a collaborative team environment.


• Post-secondary diploma or degree in Computer Science, Data Science, Information Technology, Statistics, Mathematics, Engineering, or a related field is preferred. Equivalent professional training and experience may also be considered.


• Experience within real estate, property management, or a REIT environment is considered an asset.

If you have strong experience in machine learning and predictive analytics, are a confident self-starter, and are able to work full-time in a hybrid arrangement consisting of three days per week onsite in Downtown Calgary and two days remotely,

ID# 5549

Questions

• How many years of professional experience do you have in machine learning, predictive analytics, or data science?


• Are you able to work three days per week onsite in Downtown Calgary and two days remotely?