Aroha Technologies
Lead Data Scientist (Azure Databricks)
Tampa, Florida, USA · Mid · Contract
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About the role
- 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 targets.
- Communicate model insights and forecast accuracy - to senior stakeholders through dashboards, executive briefings, and written reports - making complex model behaviour accessible to business audiences.
- MLOps & CI/CD for ML MLflow, GitHub Actions, or Azure DevOps pipelines to automate model retraining, evaluation gates, and deployment to Databricks Model Serving.
Responsibilities
- Design and maintain forecasting pipelines - at SKU, category, and regional hierarchy levels - incorporating POS data, promotional calendars, seasonality indices, and external signals (macroeconomic, weather).
- Operationalise ML models using MLflow on Databricks - manage the model registry, version control experiments, automate retraining schedules, and configure drift monitoring alerts.
- Collaborate with commercial and supply chain teams - to translate forecast outputs into inventory recommendations, production planning inputs, and revenue growth strategies.
- Mentor junior data scientists - conduct code reviews, lead knowledge-sharing sessions, support career development, and build a high-performance data science culture.
- Partner with data and platform engineers - to ensure feature pipelines on Azure Data Lake / Delta Lake are reliable, scalable, and aligned with model refresh cadence requirements.
Requirements
- You bring deep ML expertise, strong Python engineering skills, and a nuanced understanding of CPG market dynamics - and you are comfortable translating complex model outputs into clear business recommendations.
Benefits
- Drive continuous model improvement - benchmark new algorithms, evaluate AutoML approaches, and run controlled experiments to improve MAPE, bias, and coverage metrics.
This listing is sourced directly from Aroha Technologies's careers page and normalized into a canonical job model.