We are seeking a highly motivated Data Scientist to help modernize an existing production analytics platform comprised of two applications: (1) recommendations for convenience retail and FSOP, and (2) product availability. In this role, you will enhance and operationalize ML/AI solutions end-to-end—partnering with product, engineering, and business stakeholders to improve model performance, reliability, scalability, and time-to-value. You will also contribute to data engineering efforts where necessary to ensure trusted, well-modeled, and production-ready data pipelines that power these applications.
Responsibilities
Platform Modernization & Applied Data Science (Primary)
- Partner with product, engineering, and business stakeholders to modernize and scale a production platform supporting recommendations (convenience retail & FSOP) and product availability.
- Assess current models, features, and data flows; prioritize technical debt and propose a pragmatic modernization roadmap (accuracy, latency, robustness, maintainability).
- Build, validate, and deploy ML/analytics solutions using production-grade patterns (reproducible training, versioning, automated testing).
- Establish measurement and experimentation loops (offline evaluation, online testing where applicable) and quantify impact of increments released.
- Communicate tradeoffs, results, and recommendations through clear narratives and visualizations for technical and non-technical audiences.
- Operational excellence: define and monitor model/application health (data quality checks, drift detection, performance SLAs) and drive continuous improvement in partnership with platform/architecture teams.
Data Engineering – Secondary Focus
- Partner on scalable ingestion and transformation pipelines (e.g., Azure Databricks, Azure Data Factory) that support both recommendation and availability use cases.
- Implement and maintain reliable feature and training datasets, including data validation and lineage to support production ML.
- Contribute to lakehouse patterns for batch and near-real-time processing; collaborate with teams using event-streaming technologies where applicable.
- Support integration patterns (APIs, jobs, and services) required to operationalize models and analytics into the two platform applications.
What will you learn?
- Deep understanding of bottler operations and industry-specific analytics applications.
- Data science, Machine learning, and broader AI are highly impactful to achieve meaningful business outcomes. You will get to apply your skills to real-life business problems.
- Industry/FMCG trends and Benchmarks for new/emerging technologies incl. vendor roadmaps and strategic developments.
- Bottler and NAOU (North American Operating Unit, Coca-Cola Company) business strategies.
What makes you a good fit?
Minimum Qualifications
- Bachelor's degree in computer science, Statistics, Mathematics, or a related field (or equivalent practical experience).
- 4+ years in data science (or closely related applied ML/analytics role), delivering end-to-end solutions in production environments.
- Hands-on expertise building and evaluating machine learning models (e.g., scikit-learn, XGBoost, LightGBM, time-series and/or deep learning architectures).
- Proficiency in Python and SQL.
- Experience deploying and operating models in production, including monitoring, performance measurement, and iteration based on feedback.
- Ability to work within an existing platform/codebase, identify modernization opportunities, and deliver improvements incrementally without disrupting service.
Preferred Qualifications
- Experience partnering with data engineering and/or MLOps teams (or owning parts of DE/MLOps work) to productionalize ML systems (CI/CD, automated testing, release practices).
- Experience building reliable ETL/ELT pipelines and working with structured and unstructured data.
- Proficiency with Databricks, PySpark, Azure Data Factory, and Azure Data Lake (or comparable cloud tooling).
- Familiarity with common ML operations patterns (feature/training data management, lineage, reproducibility, monitoring).
- Generative AI familiarity (e.g., using LLM tools to accelerate development, improve explainability, or support analysis of workflows).
Professional & Interpersonal Skills
- Strong analytical thinker with proven problem-solving abilities.
- Exceptional written, verbal, and interpersonal communication skills.
- Adaptable; thrives in fast-paced, dynamic environments with shifting priorities.
- Collaborative team player with the ability to influence stakeholders across functions.
- Committed to fostering diversity, equity, and inclusion in the workplace.
- Consistently demonstrates CONA’s core values: Integrity, Accountability, Passion, Collaboration, and Innovation
Work Environment: CONA follows a hybrid work model requiring a minimum of 3 days (60%) in the office per week to support collaboration and development. Tuesdays and Wednesdays are