Data Scientist

About the job

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 required in-office days, and teams align on a flexible third in-office day. Attendance is reported monthly, with business travel (e.g., bottler visits, workshops, conferences) counting toward in-office requirements. CONA values coming to the office with purpose while maintaining flexibility for remote work outside of required days.

What could be the career path following this role?

While you are the owner of your career, the below represent examples of logical career paths for a Data Scientist:

• Senior Data Scientist
• Cross-discipline AI specialist
• Development Lead – BI & Analytics
• Product Architect
• Bottler or TCCC role
• Benefits (employee contribution):Health insurance
• Health savings account
• Dental insurance
• Vision insurance
• Flexible spending accounts
• Life insurance
• Retirement plan