{"id":3453389,"url":"https://alion.io/job/dataart-senior-data-scientist-2","title":"Senior Data Scientist","company":{"id":46984,"name":"DataArt","domain":"dataart.com","url":"https://alion.io/company/dataart-com","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Schema","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["Montevideo, Uruguay"],"countries":["UY"],"hiring_countries":["CO","UY"],"hiring_countries_total":2,"salary":null,"salary_estimate":{"min_usd":28000,"max_usd":73000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2679},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"GCP","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"Statsmodels","optional":false}],"status":"live","first_seen_at":"2026-08-26T00:00:00Z","employer_posted_date":"2026-10-22","last_verified_at":"2026-10-30T19:30:49Z","board_verified":true,"closed_at":null,"days_open":65,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":65},"description":"We are looking for a Senior Data Scientist to analyze complex airport operational and passenger experience challenges using statistical methods, machine learning, natural language processing, segmentation, and experimental approaches.\nIn this role, you will work closely with product, operations, and engineering teams to identify opportunities, generate actionable insights, and support strategic decision making with data. You will apply advanced analytical techniques to understand problems deeply, validate hypotheses, and guide business prioritization.\nThe Airport Touchpoint domain focuses on enhancing the passenger experience across airport operations, from arrival through boarding, while improving the operational processes that support the journey.\nThis role is centered on deep problem discovery, leveraging statistical analysis, machine learning, natural language processing, segmentation, and exploratory analytics to uncover insights, identify improvement opportunities, and provide data driven evidence for strategic decision making and prioritization. You will transform complex operational and customer experience data into actionable recommendations that drive measurable business impact.\nAnalyze complex business and operational challenges using statistical modeling, clustering, natural language processing, and exploratory data analysis.\nGenerate actionable insights and supporting evidence to guide strategic prioritization across airport teams.\nApply advanced statistical techniques including hypothesis testing, inference, and multivariate analysis.\nConduct segmentation, clustering, principal component analysis, embeddings, and other dimensionality reduction techniques.\nBuild lightweight machine learning and natural language processing models including topic modeling, classification, and semantic grouping.\nDesign experiments and apply causal inference methodologies to evaluate business impact.\nDevelop reproducible analytical pipelines and datasets using Python and SQL.\nCreate dashboards and visualizations that support executive decision making.\nCollaborate with Product, Airport Operations, Data Engineers, and other stakeholders to guide strategy through data driven insights.\nPromote analytical rigor, documentation standards, reproducibility, and data driven decision making practices.\n5+ years of experience in Data Science, Applied Analytics, or a related field.\nStrong foundation in statistics, hypothesis testing, inference, and multivariate analysis.\nExperience with segmentation, clustering, principal component analysis, and embeddings.\nHands on experience with machine learning and natural language processing techniques including topic modeling, classification, and semantic analysis.\nStrong background in experimental design and causal inference.\nProficiency in Python, including Pandas, NumPy, scikit learn, and StatsModels.\nAdvanced SQL skills.\nExperience building end to end machine learning models and reproducible analytical pipelines.\nStrong data visualization skills and experience creating decision oriented dashboards.\nAbility to communicate analytical findings effectively to executive and non technical audiences.\nExperience collaborating across cross functional teams and influencing decisions through data.\nExperience working in Agile environments.\nFluency in Spanish.\nIntermediate English for documentation and cross team collaboration.\nExperience in airport operations or logistics.\nFamiliarity with GCP tools (BigQuery, Dataform, Dataflow, Pub/Sub).\nExperience scaling analyses and collaborating closely with Data Engineers.\nCore Competences\nAnalytical curiosity and deep problem understanding.\nExperimental and impact-oriented mindset.\nAutonomy, ownership, and critical thinking.\nAbility to turn complex findings into actionable recommendations.\nClear communication with both technical and non-technical 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