Senior Data Scientist

Senior Data Scientist – Applied Machine LearningRemote3-6+ MonthsHands-on senior technical resource on a two-person KCS project team focused on developing an ML solution for identifying high-value Cisco Learning engagement opportunities.Required SkillsTraditional predictive MLStatistical modelingClassification / probability-based modelingFeature engineering and feature selectionFeature importance / ablation analysisModel evaluation and calibrationHoldout and temporal validationLeakage identification and preventionLogistic RegressionScikit-learn or comparable ML frameworkTraditional ML, feature engineering, statistical modeling, classification/probability analysis, class imbalance, and seasonality as key areas of need.Languages / DataAdvanced PythonSQLNumPyRelational/database analysisStructured + unstructured dataML LifecycleML training/evaluation workflowsExperiment trackingAutomated testing/retraining conceptsProduction-oriented ML practicesThe operating model states that Kforce work will include understanding existing signals, building experiments against individual data sources, determining associated features/dimensions, and designing/proposing feature-engineering ML stages. Deep ML expertise is mandatory.Preferred SkillsSales-domain feature engineering / predictive analyticsPropensity modeling / opportunity or lead scoringRevenue-oriented predictive analyticsCustomer adoption, consumption, or renewal modelingSurvival / time-to-event analysisNLP / text analyticsMLflowFeature Store conceptsEnterprise data and governanceImportant recruiter emphasis: Sales-domain ML/feature-engineering experience should be treated as especially important. Client Stakeholder explicitly wrote that"Experience and Expertise in ML, Feature Engineering in Sales Domain is must."Skills :J-18808-Ljbffr