Senior Data Scientist

OverviewSeeking a Senior Data Scientist to join a high-impact Personalization & Loyalty Strategy team supporting one of the largest e-commerce organizations in the United States. This team powers trillions of recommendation decisions annually and delivers highly personalized experiences to millions of customers.This role is focused on designing and building next-generation recommender systems, personalization engines, and deep learning models that influence product discovery, coupon recommendations, substitute recommendations, and shoppable recipe experiences.The ideal candidate brings hands-on experience developing large-scale recommendation systems, deep learning expertise, and a passion for turning customer behavior data into meaningful business outcomes.Location: Cincinnati, OH (Downtown – 5 Days Onsite)Experience Level: 2–10+ YearsEmployment Type: Contract / Consulting OpportunityTop Skills RequiredMust HaveRecommender Systems / Personalization ExperienceDeep Learning Model DevelopmentTensorFlow or PyTorchPythonSQLApache SparkMachine Learning Model EvaluationExperiment Design / A-B TestingStatistical AnalysisCustomer PersonalizationPreferredDatabricksAzure or GCPMLOpsData EngineeringRetail / E-Commerce ExperienceSearch Relevancy SystemsCustomer AnalyticsWhat You'll DoAs a member of the Relevancy Team, you will build and optimize recommendation engines that improve customer engagement and drive revenue growth through personalized experiences.You will work alongside data scientists, machine learning engineers, software engineers, data engineers, product managers, and business stakeholders to design, train, evaluate, deploy, and continuously improve recommendation systems operating at enterprise scale.This role offers the opportunity to solve complex machine learning challenges involving customer behavior, product affinity, loyalty engagement, and personalization strategies.Key ResponsibilitiesRecommender Systems DevelopmentDesign, build, and optimize recommendation engines for e-commerce personalization.Develop deep learning models for product recommendations, coupon recommendations, substitute recommendations, and recipe recommendations.Research and implement advanced recommendation algorithms including:Collaborative FilteringMatrix FactorizationDeep Learning RecommendersSequence ModelsEmbedding-Based ApproachesHybrid Recommendation SystemsModel Evaluation & OptimizationDefine evaluation frameworks and success metrics.Perform offline model evaluation and online experimentation.Conduct A/B testing to compare recommendation strategies.Analyze recommendation quality, diversity, and customer engagement metrics.Perform root cause analysis to improve recommendation accuracy and relevance.Personalization & Customer AnalyticsIncorporate customer preferences, shopping behavior, engagement history, and loyalty data into recommendation models.Improve personalization experiences using transactional, demographic, behavioral, and product data.Develop strategies that balance recommendation relevance with recommendation diversity.Production & Deployment SupportPartner with ML Engineers to support:Model deploymentModel servingModel monitoringModel versioningProduction pipelinesContribute to MLOps and operationalization best practices.Analytics & ReportingBuild customer analytics datasets and performance dashboards.Develop reporting solutions to monitor recommendation effectiveness.Generate actionable insights for business stakeholders.Collaboration & Knowledge SharingCollaborate closely with Data Science, Engineering, Product, and Business teams.Document technical approaches, findings, and best practices.Contribute reusable tools, libraries, and internal frameworks.Participate in technical mentoring and knowledge-sharing sessions.Required Qualifications2+ years of experience building large-scale recommender systems.Experience developing deep learning models for personalization use cases.Strong proficiency with TensorFlow or PyTorch.Strong programming skills in Python.Advanced SQL proficiency.Experience using Apache Spark for large-scale data processing.Strong understanding of:StatisticsExperimental DesignHypothesis TestingExploratory Data AnalysisMachine Learning Evaluation MetricsExperience working in cloud environments such as Azure or GCP.Strong communication and presentation skills.Ability to work independently and take ownership of initiatives.Excellent analytical and problem-solving skills.Preferred QualificationsExperience with Databricks.Experience supporting production ML systems.MLOps experience.Data Engineering experience.Retail, grocery, loyalty, or e-commerce experience.Search relevance and ranking experience.Experience working with large-scale customer behavior datasets.Technical EnvironmentPythonSQLApache SparkTensorFlowPyTorchDatabricksAzureGCPMachine LearningDeep LearningRecommender SystemsPersonalization EnginesA/B TestingMLOps