Data Scientist (All Levels)

This role builds and applies advanced analytics, machine learning, AI, and modern data engineering to support pricing, forecasting, valuation, and enterprise data platform initiatives. The position is based in Atlanta and focuses on delivering secure, production-ready models, pipelines, curated datasets, and AI-enabled tools.

Key Responsibilities

• Analyze and organize customer and market data to support structured pricing, planning, and forecasting.
• Support development and validation of reusable data pipelines and curated datasets under guidance.
• Maintain and validate existing models and recurring reports, including troubleshooting data issues.
• Develop baseline statistical or machine learning models under guidance, such as regression, classification, and forecasting.
• Use approved AI tools to automate routine analysis and reporting, including templated notebooks and prompt-driven summaries.
• Document assumptions, code, and data lineage to support audit and review requests.
• Continuously identify improvements to data quality, model performance, and operational efficiency.
• Assist with monitoring data pipeline results, investigating data quality issues, and documenting corrective actions.
• Design and develop statistical and ML models for business problems including forecasting, valuation, segmentation, and anomaly detection.
• Build and maintain scalable analytical and data engineering pipelines, reusable curated datasets, and feature assets, including validation and data quality checks.
• Develop and optimize data ingestion, transformation, and orchestration processes within Databricks and Azure environments.
• Create AI-enabled analytical tools such as guided Q&A over curated data and automated insight generation with measurable value.
• Develop dashboards and stakeholder-ready outputs, explaining model results and tradeoffs.
• Collaborate cross-functionally to define requirements, success metrics, and adoption approach, and own delivery for assigned workstreams.
• Lead end-to-end delivery of advanced analytics and AI solutions including design, build, deploy, and monitor.
• Define modeling standards, validation approaches, monitoring thresholds, and ensure explainability and audit readiness.
• Drive adoption by integrating models and AI tools into business workflows and decision processes.
• Partner with leadership to prioritize use cases, manage tradeoffs, and quantify business impact.
• Improve data quality and governance practices to support scalable AI across markets and strengthen platform reliability, performance, security, and governance with IT and enterprise data teams.
• Maintain accountability for domain AI outcomes and risk posture, including final technical approval for production readiness.
• Prioritize AI use cases based on business value, feasibility, and risk, aligned with SouthStar requirements for responsible AI use.
• Establish reusable AI/ML frameworks, templates, and best practices to accelerate delivery across teams.
• Lead cross-functional delivery of production AI solutions such as automation, forecasting, decision support, and AI assistants.
• Support workforce enablement through training, playbooks, and coaching to elevate AI adoption and productivity.
• Provide technical direction across multiple teams through influence, mentor junior staff, and conduct technical reviews for complex problem-solving.
• Stay current on GenAI, NLP, and advanced ML trends and assess applicability to SouthStar’s business.
• Provide strategic direction for SouthStar’s cloud data platform architecture in alignment with data engineering, analytics, and AI capabilities.
• Establish data engineering design standards, reusable patterns, and operating practices for secure, reliable, and scalable data products.

Required Education

• Level 04/05: Bachelor’s degree in a quantitative field (examples include mathematics, statistics, economics, data science, computer science, or similar).
• Level 06/07: Master’s degree in analytics, statistics, data science, computer science, or a related quantitative field.

Required Qualifications

• Working knowledge of SQL and relational databases (e.g., SQL Server, Oracle, MySQL).
• Hands-on programming for analysis using Python or R and basic ML or statistical modeling (Level 04).
• Ability to follow established standards for documentation, validation, and reproducibility (Level 04).
• Clear communication of results to technical and non-technical stakeholders.
• Basic understanding of data engineering concepts including data pipelines, data quality validation, and cloud-based analytics platforms (Level 04).
• Ability to develop or support reusable data transformations and follow established data engineering standards (Level 04).
• Level 05: Strong programming skills in Python or R; solid SQL proficiency, including experience with feature engineering, model evaluation, and performance tuning; experience with dashboards/visualizations (Power BI, Tableau, SSRS); and strong understanding of data governance basics.
• Level 06: Strong troubleshooting and documentation for complex analytical systems, technical leadership and stakeholder management, and ability to design validation, monitoring, and retraining plans (including drift detection and performance thresholds).
• Level 06: Hands-on experience with Delta Lake, Spark optimization, Azure Data Factory, and cloud-native data platforms.
• Level 07: Expert-level modeling breadth (including NLP, deep learning, Bayesian methods, clustering, and neural networks) plus proven ability to define reusable AI/ML frameworks, standards, and governance guardrails.
• Level 07: Experience with complex integrations or migrations across data sources and platforms such as Databricks and Azure, with partnership to set architectural direction with IT.
• Level 07: Deep experience with Databricks, Azure Data Factory, Delta Lake, Spark, and related cloud technologies, and ability to establish data engineering standards, data quality frameworks, observability, and operational practices for scalable analytics and AI.

Technologies

• SQL, SQL Server, Oracle, MySQL
• Python, R
• Databricks, Spark, Azure, Azure Data Factory, Delta Lake
• AWS
• Power BI, Tableau, SSRS
• GenAI, NLP

Location and Work Environment

This position is based at SouthStar’s corporate office in Atlanta, Georgia. Work is hybrid with four days in-office and one day remote per week (subject to change based on business needs).