Applied Data Scientist

Applied Data ScientistApplied Research Group – Supply Chain OptimizationGAINS is on a mission to make supply chains smarter, faster, and self‐improving, powered by AI. Our decision intelligence platform doesn't just support decisions, it drives them by aligning strategy, planning, and execution across every level of the supply chain. We serve inventory‐intensive industries where the stakes are high and the complexity is real, helping customers move from reactive, spreadsheet‐driven planning to continuously learning, AI‐led operations that deliver measurable results fast. At GAINS, we call it Moving Forward Faster— and it's not a tagline, it's how we're redefining what's possible in driving supply chain decisions.About the RoleAs an Applied Data Scientist on the Applied Research Group at GAINS, you will research, design, build, and deploy production ML models that directly improve supply chain outcomes for enterprise customers. This is a hybrid role that spans the full ML lifecycle—from exploratory analysis and model development through production deployment and ongoing performance tuning. Your work will address core supply chain problems where machine learning delivers measurable business value.On any given week, you might be designing a new feature engineering approach, running experiments to evaluate alternative modeling techniques, debugging model drift for a specific customer, or building pipeline infrastructure to operationalize a new ML capability. You will collaborate closely with product managers, professional services, software engineers, and customer‐facing teams to translate complex supply chain challenges into well‐scoped ML solutions.This is a hands‐on IC role with high autonomy and direct impact on customer outcomes and revenue. You will own ML projects end‐to‐end—the science and the engineering.A Day in the LifeResearch, design, and develop machine learning models for supply chain applications that drive measurable improvements in operational efficiency and planning accuracyPerform exploratory data analysis, statistical modeling, and feature engineering on large, complex supply chain datasets toidentifysignals and improve model performanceDesign and run experiments to evaluate new modeling approaches, loss functions, feature sets, and hyperparameter configurations—interpreting results and translating findings into production improvementsBuild andmaintainrobust ML pipelines that process, clean, and transform data from enterprise supply chain systems (SQL databases, APIs, ERP integrations)Deploy andmaintainmodels in cloud‐based production environments, managing the full lifecycle from training through inference and monitoringImplement model evaluation, drift detection, and monitoring frameworks to ensure reliability across diverse customer environmentsDiagnose and resolve model performance issues for individual customers—investigating data quality, feature behavior, and distributional shiftsPartner with product managers, professional services, and engineering teams to understand customer problems and scope ML solutions appropriatelyCommunicate findings, model behavior, trade‐offs, and recommendations clearly to both technical and non‐technical stakeholdersContribute to the team's technical direction on MLmethodology, architecture, tooling, and best practicesRequired QualificationsBachelor's degree in Computer Science, Statistics, Data Science, Engineering, Operations Research, or a related technical field; or equivalent professional experience3+ years hands‐on experience in applied machine learning or data science roles, with models developed and deployed to productionStrong Python skills with experience writing clean, maintainable, production‐grade ML code3+ years professional SQL experience, including complex queries against large enterprise datasetsDeep understanding of statistical and machine learning methods: gradient boosting (LightGBM,XGBoost,CatBoost), regression, decision trees, clustering, time series techniques, and model evaluationmethodologyExperience with feature engineering for structured and tabular data, including domain‐informed feature design, temporal feature construction, and feature selection techniquesDemonstrated ability to design experiments, evaluate model performance rigorously, and iterate on approaches based on empirical resultsExperience building andmaintainingML pipelines—data ingestion, feature engineering, training, evaluation, deploymentWorking knowledge of cloud‐based ML infrastructure (Azure preferred; AWS or GCP acceptable)Strong communicationskills with the ability to explain model behavior, experimental results, and trade‐offs to non‐technical audiencesSelf‐directed witha track recordof owning ML projects end‐to‐end—from problem formulation through production delivery—with minimal supervisionPreferred QualificationsMaster's or PhD in Computer Science, Statistics, Data Science, Engineering, Operations Research, or a related technical fieldExperience in supply chain, operations, orlogisticsdomainsBackground in time series modeling, probabilistic methods, or optimization techniques applied to operational problemsFamiliarity with Databricks, Spark, or similar distributed compute platforms for ML workloadsExperience with Azure services: Azure ML, Container Apps, App Configuration, DevOps pipelinesExperience working directly with enterprise customers to tune,validate, and explain model outputs in their specific business contextExperience withMLflowfor experiment tracking and model versioningExperience with Kafka or similar event streaming platforms for real‐time data integrationCuriosity about the business processes your models serve and motivation to understand how supply chain decisions areactually madeCore CompetenciesCustomer Impact:Builds solutions with the end customer in mind—measures success by business outcomes, not model metrics aloneAnalytical Depth:Goes beyond surface‐level results to understand why models behave the way they do, especially when they fail—combines scientific rigor with practical problem‐solvingEngineering Rigor:Writes production‐quality code, designs reliable pipelines, and thinks about failure modes before they happenManages Complexity:Navigates messy real‐world data and ambiguous problem definitions to deliver practical, scalable solutionsCommunicates Effectively:Translates technical model behavior and experimental findings into clear narratives for product, services, and leadership audiencesDrives Results:Takes ownership, follows through on commitments, and delivers measurable improvements to customer outcomesTechnology EnvironmentPython,LightGBM, SQL, Azure (Container Apps, ML, DevOps), Databricks, Git/GitHub. Enterprise supply chain platform with SQL Server backends and REST APIs.Why GAINSWork on software that leverages AI and ML to solve real logistics challenges for customersDirect impact on developer experience across the entire engineering orgCollaborative, low‐bureaucracy environment where engineers own their work end‐to‐endCompetitive compensation and benefitsWe are committed to equal employment opportunity and welcome everyone regardless of race, color, ancestry, religion, national origin, age, sex, gender identity, sexual orientation, disability, marital status, domestic partner status, veteran status or medical condition. We encourage people from all backgrounds to apply.#J-18808-Ljbffr