{"id":2434498,"url":"https://alion.io/job/ultradecor-ai-data-scientist","title":"AI Data Scientist","company":{"id":4428517,"name":"Ultra Decor","domain":"ultradecor.uz","url":"https://alion.io/company/ultradecor-2","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"HeadHunter","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Tashkent, Uzbekistan"],"countries":["UZ"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":19500,"max_usd":50000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1986},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"1C","optional":false},{"name":"AI Agents","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"OCR","optional":false},{"name":"Power BI","optional":false},{"name":"Python","optional":false},{"name":"QlikSense","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-09-29T06:49:43Z","employer_posted_date":"2026-09-29","last_verified_at":"2026-10-13T13:30:11Z","board_verified":true,"closed_at":null,"days_open":15,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":15},"description":"Responsibilities: Build and validate predictive / ML models for demand and sales forecasting, promo response, price elasticity and scenario (what-if) analysis.\n Deliver commercial and customer analytics: segmentation, ABC-XYZ, RFM, LTV, churn, cross- and up-sell, assortment and price-matrix analysis.\n Design and ship GenAI and agentic solutions - document processing (OCR/NLP), retrieval-augmented generation (RAG) over corporate data, copilots and assistants for business and process tasks.\n Automate routine analytics and reporting through LLM agents and scripting, cutting manual effort across the function.\n Apply process mining to map business processes, surface bottlenecks and control gaps, and measure cycle KPIs (touchless / STP, first-time-right).\n Co-design process automations with the Business Process and functional teams; 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