{"id":2139610,"url":"https://alion.io/job/southern-data-scientist-all-levels","title":"Data Scientist (All Levels)","company":{"id":2607898,"name":"Southern","domain":"southern.com","url":"https://alion.io/company/southern-com","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"staff","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Atlanta, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":124000,"max_usd":248000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":366},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"ETL/ELT","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"MySQL","optional":false},{"name":"NLP","optional":false},{"name":"Power BI","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false}],"status":"live","first_seen_at":"2026-10-02T21:47:27Z","employer_posted_date":"2026-10-02","last_verified_at":"2026-10-11T20:16:20Z","board_verified":true,"closed_at":null,"days_open":8,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":8},"description":"Data Scientist (All Levels)\nJOB SUMMARY:\nThis position develops and applies advanced analytics, machine learning, AI, and modern data engineering practices to support structured pricing products, forecasting, valuation, and enterprise data platforms. The role designs, builds, and maintains analytical models, scalable data pipelines, curated data assets, and AI-enabled tools that deliver timely, accurate insights and automation. The position is accountable for improving data quality, platform reliability, model performance, governance, explainability, and scalability across markets, and partners across the enterprise to deploy secure, production-ready data, analytical, and AI solutions.\nThis position may be filled under the following Grade Levels: Data Scientist (GL - 04), Senior Data Scientist (GL - 05), Staff Data Scientist (GL - 06), or Principal Data Scientist (GL - 07). Individuals who demonstrate advanced skills in work experience, education, and certifications may be eligible for the more advanced position.\nJOB REQUIREMENTS: (Education, Experience, Knowledge, Skills)\nData Scientist (Level 04)\nEducation\nRequired: Bachelor’s degree in a quantitative field (e.g., mathematics, statistics, economics, data science, computer science, or similar).\n\nPreferred: Master’s degree (or in progress) in a quantitative discipline\n\nExperience\n0-2 years of experience (including internships/co-ops) in analytics, data, or modeling.\n\nPreferred: Exposure to energy/utility markets or pricing/forecasting concepts (through coursework or experience).\n\nPreferred: Exposure to Databricks, Spark, Azure, or similar modern data and analytics technologies through coursework, internships, or work experience.\n\nKnowledge/Skills\nWorking knowledge of SQL and relational databases (e.g., SQL Server, Oracle, MySQL).\n\nHands-on programming for analysis (Python or R) and basic ML/statistical modeling.\n\nAbility to follow established standards for documentation, validation, and reproducibility.\n\nAble to communicate results clearly to technical and non-technical stakeholders.\n\nBasic understanding of data engineering concepts, including data pipelines, data quality validation, and cloud-based analytics platforms.\n\nAbility to develop or support reusable data transformations and follow established data engineering standards.\n\nSenior Data Scientist (Level 05)\nEducation\nRequired: Bachelor’s degree in a quantitative field (e.g., mathematics, statistics, economics, data science, computer science, or similar).\n\nPreferred: Master’s degree in analytics, statistics, data science, computer science, or similar.\n\nExperience\n3-5 years' experience in analytics/modeling and data processing\n\nDemonstrated ability to build and manage models in a business environment.\n\nExperience working with large data using SQL and modern analytics platforms (e.g., Databricks/Spark/Azure/AWS).\n\nExperience developing and maintaining production data pipelines using Databricks, Spark, Azure Data Factory, or comparable cloud technologies.\n\nKnowledge/Skills\nStrong programming skills in Python or R; solid SQL proficiency.\n\nExperience with feature engineering, model evaluation, and performance tuning.\n\nExperience with dashboards/visualizations (e.g., Power BI, Tableau, SSRS) to communicate insights.\n\nStrong understanding of data governance basics (access, quality checks, and documentation).\n\nUnderstanding of modern data engineering practices, including data ingestion, transformation, orchestration, data quality monitoring, and pipeline automation.\n\nExperience building reusable, curated datasets and supporting production analytics environments.\n\nStaff Data Scientist (Level 06)\nEducation\nRequired:Master’s degree in analytics, statistics, data science, computer science, or a related quantitative field\n\nPreferred: PhD (or in progress) in one of the above disciplines\n\nExperience\n5-10 years of experience in analytics/data science, modeling, and large-scale data work.\n\nHands-on experience delivering machine learning/AI solutions and/or production-level model deployment.\n\nExperience manipulating large databases using SQL and platforms such as Databricks/Spark/Azure/AWS.\n\nExperience designing and implementing scalable data pipelines and cloud-based data solutions in Databricks and Azure environments.\n\nKnowledge/Skills\nAdvanced modeling breadth (supervised/unsupervised methods) and strong statistical foundations.\n\nAbility to design validation, monitoring, and retraining plans (drift detection, performance thresholds).\n\nStrong troubleshooting and documentation skills for complex analytical systems.\n\nTechnical leadership and stakeholder management; able to drive alignment across teams.\n\nStrong understanding of data engineering concepts, including ETL/ELT, data modeling, pipeline orchestration, and data quality management.\n\nHands-on experience with Delta Lake, Spark optimization, Azure Data Factory, and cloud-native data platforms.\n\nPrincipal Data Scientist (Level 07)\nEducation\nRequired: Master’s degree in analytics, statistics, data science, computer science, or a related quantitative field\n\nPreferred: PhD in one of the above disciplines; Project Management Professional (PMP) or equivalent leadership certification\n\nExperience\n10+ years of experience in analytics/data science, including a strong track record delivering enterprise-scale data and AI solutions.\n\nExtensive experience manipulating large data using SQL and platforms such as Databricks/Spark/Azure/AWS.\n\nDemonstrated success defining and scaling AI capabilities, frameworks, or platforms with strong execution.\n\nDemonstrated experience leading enterprise data platform and data engineering initiatives in Databricks and Azure environments.\n\nKnowledge/Skills\nExpert-level modeling breadth (e.g., NLP, deep learning, Bayesian methods, clustering, neural networks) and strong statistical foundations.\n\nProven ability to define reusable AI/ML frameworks, standards, and governance guardrails.\n\nExperienced in complex integrations/migrations across data sources and platforms such as Databricks, Azure; sets architectural direction in partnership with IT\n\nDemonstrated leadership in analytical model design/ development/ testing/ troubleshooting/ documentation for complex analytical systems\n\nStrong stakeholder management capabilities and a proven ability to align teams\n\nExpert understanding of modern data architecture, data modeling, ETL/ELT design, pipeline orchestration, and cloud-based data engineering practices.\n\nDeep experience with Databricks, Azure Data Factory, Delta Lake, Spark, and related cloud technologies.\n\nAbility to establish data engineering standards, data quality frameworks, observability, and operational practices for scalable analytics and AI solutions.\n\nMAJOR JOB RESPONSIBILITIES:\nData Scientist (Level 04)\nSummary: Applies analytical and ML techniques under guidance to support forecasting/valuation and build reusable, well-documented analyses.\nAnalyze and organize customer/market data for pricing, planning, and forecasting.\n\nSupport the development and validation of reusable data pipelines and curated datasets under guidance.\n\nMaintain and validate existing models and recurring reports; troubleshoot data issues.\n\nDevelop baseline statistical or ML models under guidance (e.g., regression, classification, forecasting).\n\nUse approved AI tools to automate routine analysis and reporting (e.g., templated notebooks, prompt-driven summaries).\n\nDocument assumptions, code, and data lineage; support audit and review requests.\n\nContinuously identify small improvements to data quality, model performance, and efficiency.\n\nAssist with monitoring data pipeline results, investigating data quality issues, and documenting corrective actions.\n\nSenior Data Scientist (Level 05)\nSummary: Independently designs and delivers advanced analytics and machine learning solutions and reusable pipelines for business use cases\nDesign and develop statistical and ML models for business problems (forecasting, valuation, segmentation, anomaly detection).\n\nBuild and maintain scalable analytical and data engineering pipelines, reusable curated datasets, and feature assets; implement validation and data quality checks.\n\nDevelop and optimize data ingestion, transformation, and orchestration processes within Databricks and Azure environments.\n\nCreate AI-enabled analytical tools (e.g., guided Q&A over curated data, automated insight generation) with measurable value.\n\nDevelop dashboards and stakeholder-ready outputs; explain model results and tradeoffs.\n\nCollaborate cross-functionally to define requirements, success metrics, and adoption approach.\n\nOwn delivery for assigned workstreams.\n\nStaff Data Scientist (Level 06)\nSummary: Leads advanced analytics and AI initiatives end-to-end, establishing standards and ensuring scalable, governed delivery.\nLead end-to-end delivery of advanced analytics and AI solutions (design, build, deploy and monitor).\n\nDesign, develop, and maintain scalable cloud-based data pipelines and curated datasets that support analytics, reporting, AI, and business operations.\n\nDefine modeling standards, validation approaches, and monitoring thresholds; ensure explainability and audit readiness.\n\nDrive adoption by integrating models and AI tools into business workflows and decision processes.\n\nPartner with leadership to prioritize use cases, manage tradeoffs, and quantify business impact.\n\nContinuously improve data quality and governance practices to support scalable AI across markets.\n\nPartner with IT and enterprise data teams to improve platform reliability, performance, security, and data governance.\n\nPrincipal Data Scientist (Level 07)\nSummary: Serves as SouthStar’s senior technical authority for Data, Analytics and AI strategy, setting standards and priorities to ensure secure, scalable, and production-ready solutions.\nAccountable for domain AI outcomes and risk posture, including final technical approval for production readiness\n\nPrioritize AI use cases based on business value, feasibility, and risk.\n\nDefine and enforce SouthStar standards for model development, validation, monitoring, documentation, and responsible AI use, aligned with enterprise frameworks.\n\nEstablish reusable AI/ML frameworks, templates, and best practices to accelerate delivery across teams.\n\nLead cross-functional delivery of production AI solutions (automation, forecasting, decision support, AI assistants).\n\nDrive workforce enablement (training, playbooks, coaching) to elevate AI adoption and productivity.\n\nProvide technical direction across multiple teams and functions, leading through influence rather than formal authority.\n\nMentor junior staff on modeling practices and documentation; conduct technical reviews and guide complex problem-solving\n\nStay current on GenAI, NLP, and advanced ML trends and assess their applicability to SouthStar’s business.\n\nProvide strategic direction for SouthStar’s cloud data platform architecture, ensuring alignment across data engineering, analytics, and AI capabilities.\n\nEstablish data engineering design standards, reusable patterns, and operating practices for secure, reliable, and scalable data products.\n\nLocation and Work Environment\nThis position is based at SouthStar's corporate office in Atlanta, Georgia, with a hybrid work schedule of four days in-office and one day remote per week (subject to change per business needs). The work environment is dynamic, fast-paced, and challenging with competing demands.\nDisclaimer:\nThis information describes the general nature and level of work performed by employees in this job. The description is not designed to be a comprehensive inventory of duties, responsibilities and qualifications required in the job. 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