{"id":4208876,"url":"https://alion.io/job/georgia-pacific-data-scientist","title":"Data Scientist","company":{"id":1770798,"name":"Georgia-Pacific","domain":"gp.com","url":"https://alion.io/company/gp-com","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":"full_time","work_mode":"on_site","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":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Cursor","optional":false},{"name":"Devin","optional":false},{"name":"GitHub","optional":false},{"name":"Machine Learning","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"SAS","optional":false},{"name":"SQL","optional":false},{"name":"Time Series","optional":false}],"status":"live","first_seen_at":"2026-11-13T00:12:36Z","employer_posted_date":null,"last_verified_at":"2026-11-13T00:12:36Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"Georgia-Pacific is hiring a Data Scientist to support its manufacturing operations from its Atlanta Collaboration & Support Center. The role develops and deploys scalable statistical, machine learning, and deep learning models, and partners with business and engineering teams to improve facility safety and operational efficiency.\n\nResponsibilities\nAnalytics Project Ownership: Own all aspects of analytics projects from start to finish – pursuing innovative ideas, developing models, uncovering, communicating actionable insights, and implementing solutions to capture value\nWorks with various engineering teams, owns end-to-end solution development and scaling focused on Operations (manufacturing) challenges\nPartnership: Develop and maintain relationships with key business partners to enable improved decision making\nFocus needs to remain on building minimum viable product towards solving majority of the issue instead of “perfecting” the solution, unless critical to safety\nAbility to operate independently and self-manage\n\nSkills\nBachelor's Degree or higher in a field related to data science such as Engineering Mathematics or Business Analytics or Statistics or Computer Science or Data Science\n2+ years of experience creatively applying machine learning, outside of academic datasets, using a formal programming language (Python or R or SAS)\nExperience in data science that encompass having models deployed to production with successful outcomes for users\nExperience with unsupervised and supervised machine learning\nExperience working with large data sets\nExperience with cleaning, manipulating, analyzing, and modeling time series data\nExperience with writing and executing complex SQL queries\nAbility to manage and deliver multiple projects in a fast-paced environment\nAbility to work effectively in a collaborative team environment\nExperience with code version control platforms such as GitHub or GitLab\nStrong communication and collaboration skills, with experience gathering, translating, and implementing business requirements and feedback into technical solutions\nLegal authorization to work permanently in the United States for any employer without requiring visa sponsorship\nMaster's Degree or higher in a field related to data science such as Data Science, Mathematics, Engineering, Statistics, Computer Science, or Business Analytics\nExperience in SAS Code for the purpose of statistics or machine learning\nExperience manipulating data, computing, memory, optimization; experience with cloud computing (AWS)\nExperience with SAS Event Stream Processing (ESP)\nExperience working with Manufacturing Operations\nExperience working with loT data\nProven experience designing, building, and productionizing GenAI solutions, e.g. prompt engineering, RAG, LLMs, Agentic AI and model evaluation\nKnowledge of MLOps best practices, including CI/CD pipelines and model lifecycle management\nExperience using AI-powered development tools and coding agents (e.g., Devin, Cursor) to improve productivity\n\nBenefits\nMedical, dental, and vision benefits\nFlexible spending and health savings accounts\nLife insurance\nADD insurance\nDisability benefits\nRetirement benefits\nPaid vacation/time off\nEducational assistance\nInfertility assistance, paid parental leave, and adoption assistance may also be included\nSpecific eligibility criteria are set by the applicable Summary Plan Description, policy, or guideline, and benefits may vary by geographic region.\n\nCompany Overview\nGeorgia-Pacific and its subsidiaries are among the world’s leading manufacturers and marketers of bath tissue, paper towels and napkins, tableware, paper-based packaging, cellulose, specialty fibers and building products. It was founded in 1927, and is headquartered in Atlanta, Georgia, USA, with a workforce of 10001+ employees. 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