Data Scientist (Fulltime)

Data Scientist – Analytics Platform Modernization Location: Atlanta, GA (Hybrid | On-site 3 days/week minimum) Type: Full-Time | Local Candidates Only About the Role We are seeking a highly motivated Data Scientist to help modernize an existing production analytics platform comprised of two applications: (1) a recommendations engine for convenience retail and foodservice operations, and (2) a product availability solution. 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 and product availability use cases. • Assess current models, features, and data flows; prioritize technical debt and propose a pragmatic modernization roadmap focused on accuracy, latency, robustness, and maintainability. • Build, validate, and deploy ML/analytics solutions using production-grade patterns including reproducible training, versioning, and automated testing. • Establish measurement and experimentation loops (offline evaluation, online testing where applicable) and quantify the impact of incremental releases. • Communicate tradeoffs, results, and recommendations through clear narratives and visualizations for both technical and non-technical audiences. • Define and monitor model and application health — including data quality checks, drift detection, and performance SLAs — and drive continuous improvement in partnership with platform and architecture teams. Data Engineering (Secondary) • Partner on scalable ingestion and transformation pipelines (e.g., Azure Databricks, Azure Data Factory) supporting 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. Minimum Qualifications • Bachelor's degree in Computer Science, Statistics, Mathematics, or a related field (or equivalent practical experience). • 4+ years in data science or a 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 directly owning 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 including feature/training data management, lineage, reproducibility, and monitoring. • Generative AI familiarity (e.g., using LLM tools to accelerate development, improve explainability, or support workflow analysis). 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. Work Environment This is a hybrid role requiring a minimum of 3 days (60%) in the office per week. Two anchor in-office days are required each week, with teams aligning on a flexible third day. Business travel (e.g., client visits, workshops, conferences) counts toward in-office requirements. Candidates must be local to the Atlanta, GA metro area. Relocation assistance is not available for this position. Career Path While you are the owner of your career, logical next steps for this role may include Senior Data Scientist, Cross-Discipline AI Specialist, Development Lead – BI & Analytics, Product Architect, and other advanced technical or leadership roles.