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Added 25 days ago
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Predictive Analytics Specialist (ID#5549)
New Value Solutions
Calgary, AB
- Contract
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New Value Solutions

Predictive Analytics Specialist (ID#5549)

Calgary, AB, Canada

Contract

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Job details

Job type: Contract
Apply by: Oct 28, 2026
Experience: Minimum 3+ years of experience in data science, machine learning, predictive analytics, advanced analytics, or a related area.
Posting language: English
Posted: Sep 28, 2026

Original job description

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, please submit your resume. While we thank all candidates in advance for their application, only those candidates who are shortlisted will be contacted.

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?

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More about this job

Responsibilities

Design, develop, deploy, and maintain predictive and machine-learning models for real estate operations, including resident churn, occupancy, revenue, budgeting, maintenance, and supply-chain needs. Build data pipelines, databases, dashboards, and reports, monitor model performance, and collaborate with business and IT teams to turn analytical findings into actionable recommendations.

Requirements

Requires at least three years of experience in data science, machine learning, predictive analytics, or a related field, with hands-on predictive modeling, Python, SQL, and experience working with large datasets. Candidates should have experience with forecasting and model evaluation, relevant analytics and Microsoft data technologies, and strong analytical and communication skills; a related post-secondary diploma or degree is preferred.

Skills

Machine Learning
Predictive Analytics
Python
Statistical Modeling
Forecasting
SQL
Data Modeling
Azure Data Factory
Power BI
Data Engineering
Business Intelligence
Data Quality
Model Performance Monitoring
DAX
Communication
Problem-Solving

Visa sponsorship

Not detected in the job text

Keywords

Machine Learning
Predictive Analytics
Data Science
Statistical Modeling
Forecasting
Python
SQL
Pandas
NumPy
Scikit-Learn
XGBoost
TensorFlow
PyTorch
Azure Data Factory
Power BI
Azure Data Lake
SQL Server
DAX
Data Pipelines
Data Modeling
Semantic Models
Business Intelligence
Data Mining
Data Governance
Model Accuracy
Data Drift
Real Estate
Property Management
REIT
Net Operating Income

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