Data

Scientist(AI&Data;

Engineering)External

Data&Intelligent;

Insights Location Toronto,ON(Hybrid) Role

Summary Ourclientisaglobalrealestateinvestor,developer,andmanagerconnectspeopletoexceptionalplacesacrosstheoffice,logistics,residential,andretailsectors,operatinganeliteinternationalportfoliowithafocusonlong-termvaluecreation.

Weareseekingahigh-performing

Data

Scientistwithstrongdataengineeringcapabilitiestobuildadvancedanalyticalmodelsandintelligentinsights.

Inthisrole,youwillcombineexternalmarketdata(e.g.,CoStar,JLL,CBRE)withinternalenterprisedatasetswithinourmodern

Microsoft

Fabricdataplatform.

Youwilloperateacrossthefulldatalifecycle:dataingestion,transformation,predictivemodeling,andinsightgeneration.

Yourprimaryfocuswillbeintegratingmulti-sourceexternaldata,engineeringgovernedandreusabledatasets,anddevelopingpredictiveframeworksthatunlockhigh-valuebusinessintelligenceacrossenterpriseusecases.

Key

Responsibilities 1.

Data

Integration&Engineering;(Hands-on) Ingestexternaldatasets: Sourceandintegratevendordataincludingmarket,macro,demographic,andrentaldatasets.

Blendenterprisedata: Combineexternaldatawithinternalsystemslike

Yardi,assetmanagementsystems,andinvestmentdatabases.

Build

Fabricpipelines: Developrobustpipelinesusing

Microsoft

Fabric,including

Dataflows,Notebooks,Lakehouse,and

Data

Pipelines.

Implement

Medallionarchitecture: Designandmaintainstructuredlayers:Bronze(raw),Silver(cleansed/standardized),and

Gold(business-ready).

Modelreusabledatasets: Developscalabledatamodelsthatsupportcross-domainanalytics,multi-sourcecomparisons,andmachinelearning(ML)consumption. 2.

Advanced

Analytics&Predictive;

Modeling BuildMLmodels: Developmachinelearningmodelstoidentifytrends,predictoutcomes(e.g.,assetperformance,leasingrisk,marketmovements),anddetectanomalies.





Performadvancedstatisticalanalysis: Executetime-seriesmodeling,multivariateanalysis,andscenariomodeling.

Translatebusinessquestions: Convertambiguousbusinessproblemsintostructuredanalyticalframeworksandpredictivemodels. 3.

Insight

Generation&Business;

Impact Extractactionablevalue: Identifyperformancedrivers,marketopportunities,andrisksignalsfromcombineddatasources.

Deliverdataproducts: Createinsight-ready

Gold-layerdatasetsoptimizedfor

PowerBI,downstreamapps,andAIconsumption.

Communicatecomplexnarratives: Presenttechnicalfindingstonon-technicalbusinessstakeholdersusingclear,contextualstorytelling. 4.MLOps&Productionization; ManageMLlifecycle: Handleend-to-endmodeltraining,validation,hyperparametertuning,deployment,andperformancemonitoring.

Embedproductionpipelines: Integratemodelsinto

Fabricpipelinesforautomatedbatchandscheduledinference.

Ensurescalability: Buildreliable,reusable,andscalablemodelsthatservemultipleenterpriseusecases. 5.

Data

Governance&Quality; Ensuredataintegrity: Maintainhighquality,consistency,andlineageacrossdisparateexternalandinternaldatasources.

Prioritizeexplainability: Buildtransparentmodelswithclearassumptions,drivers,andtraceability.

Alignwithstandards: Strictlyadheretoenterprisedatagovernance,privacy,andsecuritypolicies. 6.

Collaboration&Delivery; Partneracrossteams: Workcloselywiththe

Delivery

Manageronvendorcoordination,Data

Engineersonpipelinearchitecture,and





Business

Leadersonuse-casedefinitions.

Driveoutcomes: Convertcomplexdatadiscoveriesintostrategicrecommendationsthatdrivemeasurablebusinessvalue. (Optional) SupportAIenablement: ContributetofutureAI-drivenworkflows,insightautomation,andsmartrecommendationengines(Note: Primaryfocusisdataandmodeling,notAIagentdevelopment).

Required

Qualifications Experience Industry

Experience: 58+yearsofprofessionalexperiencein

Data

Science,Machine

Learning,or

Advanced

Analytics.

Proven

Track

Record: Demonstratedexperienceworkingwithlarge,multi-sourcedatasetsanddeployingproduction-grademodels.

Technical

Skills Core

Languages: Expertproficiencyin

Python(Pandas,PySpark,scikit-learn,etc.)andadvancedSQL.

Data

Engineering: Stronghands-onexperiencewithdatatransformation,modelingprinciples,andETL/ELTpipelines.

Contemporary

Platforms: Directexperienceworkingwith

Microsoft

Fabric(orequivalentmodernclouddataplatformslike

Databricksor

Snowflake).

Architecture: Solidunderstandingof

Medallionarchitectureanddatawarehousingconcepts.

Analytical&Business;

Skills Methodologies: Practicalexpertiseinregression,classification,clustering,andtime-seriesforecasting.

Problem-Solving: Abilitytoextractclearsignalsfromcomplex,noisy,andunstructureddatasets.

Communication: Strongcapabilitytobridgethegapbetweentechnicalexecutionandbusinessstrategy.

Nice-to-Have

Qualifications Experiencewithrealestate,investmentmanagement,orpropertytechdatasets.

Directexperiencehandlingdatafeedsfromexternalproviderslike

CoStar,JLL,orCBRE.

Exposureto

AzureAIFoundry,LLM-basedsolutions,RAGarchitectures,oradvancedAIworkflows.

Experiencebuildingandscalingend-to-enddataproductsfromthegroundup.

📌 Data Scientist/Data Engineer (Toronto)
🏢 hireVouch
📍 Toronto