Description

Key Responsibilities
· Maintain and support the existing Product Recommendation Engine codebase in Databricks.
· Execute recurring recommendation refreshes across deployed markets and ensure updated recommendations are successfully generated and delivered downstream.
· Manage the end-to-end refresh process from PRE pipeline execution through delivery of recommendations into the sales application.
· Conduct detailed validation of recommendation outputs to ensure recommendations are accurate, complete, and consistent with expected business and model logic.
· Validate recommendations within the SRA iPad application to ensure the UI/UX is rendering correctly and the appropriate recommendations, supporting information, and functionality are displayed for each account.
· Coordinate and oversee larger validation efforts when recommendation updates or new markets are released.
· Troubleshoot issues across the recommendation pipeline and work with Data Engineering, MLOps, and application development teams to identify and resolve root causes.
· Develop and maintain unit tests and other automated validation checks to improve code quality and reduce the risk of issues reaching production.
· Monitor recommendation quality and investigate unexpected model outputs,missing recommendations, data issues, and other anomalies.
· Analyze feedback from sales teams and commercial stakeholders and translate that feedback into potential changes to model logic or business rules.
· Continuously improve the existing recommendation engine based on field feedback, model performance, and evolving business requirements.
· Develop enhancements to address recommendation challenges such as portfolio cannibalization, seasonality, account/product suitability, and other opportunities identified through sales feedback.
· Conduct exploratory analysis and model development when additional analytical or Machine Learning capabilities are needed to improve recommendation quality.
· Serve as a primary Data Science point of contact for commercial stakeholders and sales teams supporting the Product Recommendation Engine.
· Coordinate across Data Science, Data Engineering, MLOps, application development, and business teams to ensure dependencies, issues, and deliverables are addressed.
· Help manage project timelines, validation activities, business requirements, and follow-up actions associated with recommendation refreshes and releases.
· Provide regular updates to stakeholders on refresh status, validation results, issues, risks, and planned improvements.
· Document model logic, business rules, validation procedures, known issues, and changes to the recommendation engine.

Experience / Skills Required
· 3+ years of experience in Data Science, Machine Learning, Advanced Analytics,or a related quantitative role.
· Strong Python and PySpark skills.
· Strong Databricks experience.
· Strong SQL skills.
· Experience maintaining and working within an existing production Data Science or Machine Learning codebase.
· Experience developing, evaluating, and improving Machine Learning models or recommendation systems.
· Strong understanding of Machine Learning, statistical modeling, feature engineering, and model evaluation.
· Experience with Git/GitHub and working collaboratively within a shared codebase.
· Experience developing unit tests and performing structured code and output validation.
· Strong troubleshooting and root-cause analysis skills, including the ability to investigate issues across data, code, model outputs, and downstream systems.
· Ability to independently manage recurring production processes and ensure activities are completed accurately and on schedule.
· Strong project management and organizational skills with the ability to coordinate activities across multiple technical and business teams.
· Strong communication skills and the ability to translate business and sales feedback into clear Data Science requirements.
· Comfortable working directly with business stakeholders and taking ownership of issues through resolution.

Preferred
· Experience developing or supporting recommendation engines, ranking models, personalization models, or next-best-action/product solutions.
· Experience supporting a production ML solution where model outputs are consumed by a downstream application.
· Experience working with sales, customer, commercial, or consumer datasets.
· Experience with experimentation, A/B testing, and measurement of incremental model impact.
· Experience with MLflow for experiment tracking and model management.
· Experience working in GCP or another major cloud environment.
· Familiarity with CI/CD and production ML workflows.
· Experience identifying and implementing improvements to existing models based on end-user or business feedback.
· Experience working with LLM / GenAI applications is a plus

Key Skills

Education

Bachelor's degree