Machine Learning Engineer

AGS LLC; Duluth (GA); On-site; USD 120,000 - 180,000; Full time

Job summary

AGS LLC in Duluth, GA is seeking a Machine Learning Engineer to design, build and deploy predictive models that turn gameplay data into actionable insights for game design and business decisions.

You will bridge behavioral data science and production ML engineering, develop models for retention and performance, and productionize pipelines on AzureML/Fabric with MLflow for monitoring and retraining.

Strong Python/SQL skills and experience with messy real-world data are essential.

Qualifications

• Extensive data science/ML engineering experience with production deployment.
• Behavioral analytics expertise in retention, churn, or engagement models.
• Strong Python and SQL skills with libraries like pandas, scikit-learn, XGBoost, statsmodels.
• Statistical rigor including survival analysis, A/B testing, and causal inference.
• Machine learning breadth across classification, regression, clustering, and recommendations.

Responsibilities

• Build player session behavioral models from iGaming data.
• Develop game performance prediction models from features and historical data.
• Analyze and optimize mathematical models for RTP, hit frequency, and bonuses.
• Create player segmentation models to inform design and operator recommendations.
• Support the Interactive Yield Max tool with predictive models for floor positions and demographics.
• Build predictive maintenance models using telemetry to detect failures.
• Translate model outputs into designer-friendly insights for game specs.
• Design and analyze A/B tests for game-math variants.
• Productionize models on AzureML/Fabric with MLflow-based registry, monitoring, and retraining Pipelines.

Skills

Data science experience

Behavioral analytics

Python

SQL

XGBoost

Statsmodels

Machine learning breadth

Data communication

Education

Bachelor's or Master's in Data Science/Statistics/CS/Mathematics

Tools

Python

SQL

XGBoost

Statsmodels

Job description

Position Title: Machine Learning Engineer

Description

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

Equal employment opportunity, including veterans and individuals with disabilities.

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