Machine Learning Engineer

AGS, LLC
Duluth, United States of America
yesterday

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English

Job location

Duluth, United States of America

Tech stack

Clickstream
Cluster Analysis
Machine Learning
Azure
Spark
Scikit Learn
Statistics Packages
XGBoost
Machine Learning Operations

Job description

  • Buildplayersessionbehavioralmodelsretentionprediction,abandonmentmodeling,post-bonusbehavioranalysis,andbetescalationmodelingfromiGamingsessiondata
  • DevelopgameperformancepredictionmodelspredictWPUPD,timeondevice,andfloorlongevityfromgamespecificationfeaturesandhistoricalperformancedata,usingagamefeatureextractionpipelinethatreverse-engineersexistingtitlesintostructured,reusablefeatures
  • Buildmathmodeloptimizationanalyticsanalyzeactualvs.theoreticalRTP,hitfrequency,andbonusfrequency;identifymathmodelanomaliesacrossthedeployedfleet
  • Createplayersegmentationmodelsclusterplayersintobehavioralarchetypes(bonushunters,jackpotchasers,basegamegrinders)toinformgamedesignandoperatorrecommendations
  • SupporttheInteractiveYieldMaxyield-managementtoolbuildtheunderlyingmodelsthatpredictwhichAGSgamemaximizesperformanceinagivenfloorposition,operatorproperty,andplayerdemographic
  • Buildpredictivemaintenancemodelsanalyzecabineterrorlogsand,assensor/telemetrypipelinesmature(DynamicsFieldService/Dataverse),incorporatetelemetrytoidentifyfailureprecursorpatternsandpredictcomponentfailures
  • Feedgamedesigndecisionstranslatemodeloutputsintogamedesigner-friendlyinsightsthatareactionableinthegamespecificationprocess
  • DesignandanalyzeA/Btestsexperimentaldesign,statisticalanalysis,andresultsinterpretationforgamemathvarianttesting(whereregulatorilypermitted)
  • ProductionalizemodelspackagemodelsfordeploymentonAzureML/Fabric,withMLflow-basedregistry,monitoring,andretrainingpipelines

Requirements

  • 48 years of data science and/or ML engineering experience, with demonstrated production model deployment (not just notebook analysis)
  • Behavioralanalyticsexpertisehasbuiltretention,churn,orengagementmodelsusingevent-levelbehavioraldata(sessionlogs,clickstreams,transactionsequences)
  • StrongPythonandSQLskillspandas,scikit-learn,XGBoost,statsmodels;canquerythedatawarehouseindependently(amixofon-premSQLServerandSalesforcetoday,migratingtoMicrosoftFabric/OneLake)withoutrelyingonadataengineerforeveryanalysis
  • Statisticalrigorsurvivalanalysis,A/Btestdesign,causalinference,regressionmodeling;understandsthedifferencebetweencorrelationandcausation
  • Machinelearningbreadthclassification,regression,clustering,recommendationsystems;canselecttherightmodelingapproachforeachproblem
  • Datacommunicationskillscantranslatemodeloutputsintobusiness-friendlylanguagethatgamedesignersandcommercialleaderscanacton
  • Experiencewithmessy,real-worlddatacomfortablewheregamefeaturesaren'tfullydocumentedandpipelinesarestillbeingbuilt;doesn'trequireperfectdatatodelivervalue
  • Bachelor'sorMaster'sdegreeinDataScience,Statistics,ComputerScience,Mathematics,orrelatedquantitativefield

Preferred

  • Gaming,mobilegaming,orconsumerbehavioralanalyticsexperience
  • FamiliaritywithcasinogamemechanicsRTP,volatility,Hold&Spin,theoindex
  • ExperiencewithtimeseriesanalysisandanomalydetectionforIoT/sensordata
  • Knowledgeofresponsiblegamblingdataconsiderations
  • Experience with MLflow, Azure ML, or Fabric Notebooks/Spark for model lifecycle management

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