Position Title: Machine Learning Engineer
Description
JobOverview
TheDataScientist/MLEngineerbuildsanddeployspredictivemodelsandanalyticalsystemsthatturnAGS'splayerandgamedataintoquantitativeinsightsthatdirectlyimprovegamedesignandcommercialdecisions.Thisrolebridgesbehavioraldatascience(understandinghowplayersinteractwithgames)andproductionMLengineering(deployingmodelsthatactuallyreachdecision-makers).Itfeedsgamedesignerswithdata-drivendesignrecommendationsfortheML-drivengamedesigninitiative,supportsyieldmanagementwithpredictivemodelsforInteractiveYieldMax,andenablesoperatorstounderstandtheirplayerbasemoredeeplyanchoredtoAGS'sTech&DataheromissionofanaccessibledatalayerwithliveKPIspoweringeverydecision.
Responsibilities
- 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
Skills/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
Note:Alloffersarecontingentuponsuccessfulcompletionofabackgroundcheck
*Postedpositionsarenotopentothirdpartyrecruitersandunsolicitedresumesubmissionswillbeconsideredfreereferrals.
AGSisanequalopportunityemployer
Equal Opportunity Employer, including disability/protected veterans
PI286010121