Lead Data Scientist (Azure Databricks)Location: Tampa, Florida, United StatesWork Arrangement: On-siteInterview Mode: VideoJob Type: ContractPosition OverviewLead end-to-end sales forecasting model development from data sourcing and feature engineering through model training, validation, and productionisation on Databricks (Azure).Must-Have Skillsapplied ML CPG, FMCGDatabricksPython — pandas, PySpark, scikit-learnExperience with Azure ML or Azure ecosystemSecondary SkillsMLOps & CI/CD for ML – MLflow, GitHub Actions, or Azure DevOps pipelines to automate model retraining, evaluation gates, and deployment to Databricks Model ServingKey ResponsibilitiesLead end-to-end sales forecasting model development — from data sourcing and feature engineering through model training, validation, and productionisation on Databricks (Azure)Design and maintain forecasting pipelines — at SKU, category, and regional hierarchy levels — incorporating POS data, promotional calendars, seasonality indices, and external signals (macroeconomic, weather)Apply CPG domain knowledge — to model promotional uplift, new product introduction curves, product cannibalization, and retailer sell-in/sell-out dynamics into ML features and targetsOperationalise ML models using MLflow on Databricks — manage the model registry, version control experiments, automate retraining schedules, and configure drift monitoring alertsCollaborate with commercial and supply chain teams — to translate forecast outputs into inventory recommendations, production planning inputs, and revenue growth strategiesDefine and enforce data science best practices — modelling standards, experiment documentation, code review guidelines, and reproducibility requirements across the teamMentor junior data scientists — conduct code reviews, lead knowledge-sharing sessions, support career development, and build a high-performance data science cultureCommunicate model insights and forecast accuracy — to senior stakeholders through dashboards, executive briefings, and written reports — making complex model behaviour accessible to business audiencesDrive continuous model improvement — benchmark new algorithms, evaluate AutoML approaches, and run controlled experiments to improve MAPE, bias, and coverage metricsPartner with data and platform engineers — to ensure feature pipelines on Azure Data Lake / Delta Lake are reliable, scalable, and aligned with model refresh cadence requirementsAdditional RequirementsMaster's or PhD in Statistics, CS, or related field (preferred)Prior experience in working on Agile/Scrum projects with exposure to tools like Jira/Azure DevOpsProvides regular updates, proactive and due diligent to carry out responsibilitiesCommunication & Interpersonal SkillsCommunicate effectively with internal and customer stakeholdersCommunication approach: verbal, emails and instant messagesStrong interpersonal skills to build and maintain productive relationships with team membersProvide constructive feedback during code reviews and be open to receiving feedback on your own codeCapability to troubleshoot and resolve issues efficiently