{"id":1580137,"url":"https://alion.io/job/agilityio-data-scientist","title":"Data Scientist","company":{"id":669873,"name":"AgilityIO","domain":"agilityio.com","url":"https://alion.io/company/agilityio-2","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":75,"open_postings":4,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-16T05:45:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":"full_time","work_mode":"remote","remote_scope":"stated_regions","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":[],"countries":[],"hiring_countries":["AR","BR","MX","AG","AW","BS","BB","BZ","BM","BO","VG","BQ","KY","CL","CR","CW","DM","DO","EC","SV","FK","GF","GD","GP","GT","GY","HT","HN","JM","MQ","MS","NI","PA","PY","PE","PR","KN","LC","PM","SX"],"hiring_countries_total":45,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"Matplotlib","optional":false},{"name":"NumPy","optional":false},{"name":"Plotly","optional":false},{"name":"Power BI","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SciPy","optional":false},{"name":"Seaborn","optional":false},{"name":"SQL","optional":false},{"name":"Statsmodels","optional":false},{"name":"Tableau","optional":false},{"name":"Amazon Redshift","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"BigQuery","optional":true},{"name":"GCP","optional":true},{"name":"Git","optional":true},{"name":"Google BigQuery","optional":true},{"name":"Snowflake","optional":true}],"status":"live","first_seen_at":"2026-09-30T19:29:53Z","employer_posted_date":"2026-09-30","last_verified_at":"2026-10-16T17:04:36Z","board_verified":true,"closed_at":null,"days_open":16,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":16},"description":"Company Overview\nAgilityIO is a software development firm that designs and builds custom applications for startups and Fortune 500 companies. We are a global team of over 400 developers, QA engineers, project managers, and UX/UI designers with offices in New York City and Vietnam.\nThis is a flexible, contract-based role open to candidates in Latin America.\nAbout the Role\nWe are seeking a Data Scientist to evaluate AI-generated data analyses on real datasets, stress-test the reasoning behind them, and write the judgments that teach the next generation of models what rigorous data work looks like. The newest models can spin up a full analysis in minutes - load the data, pick a method, produce charts and a confident conclusion. Someone has to check whether the statistics actually hold up, and that's this role.\nResponsibilities\nEvaluate AI-built analyses on real datasets: the method choice, the assumptions, and whether the conclusion actually follows from the numbers\n\nRed-team the statistics to expose leakage, p-hacking, confounded comparisons, and confident nonsense before real users trust them\n\nWrite the better analysis when the model falls short: sound methodology, honest uncertainty, clear takeaways\n\nBuild, train, and evaluate machine learning models (classification, regression, clustering, forecasting), including feature engineering, cross-validation, and metric selection\n\nDocument methodology, assumptions, and limitations so that work can be reviewed and reproduced\n\nCollaborate with engineers, product managers, and other team members, and turn open-ended questions into well-defined analytical problems\n\nRequired Qualifications\nProfessional, academic, or serious independent experience doing real data analysis in industry or research\n\nProficiency in Python and its data stack (pandas, NumPy, SciPy, scikit-learn, statsmodels, or equivalents) and in SQL\n\nExperience working in Jupyter or similar notebook environments\n\nExperience with data visualization tools and libraries (matplotlib, seaborn, Plotly, or BI tools such as Tableau or Power BI)\n\nWorking knowledge of machine learning fundamentals: model selection, overfitting, evaluation metrics, and validation strategies\n\nClear written and spoken English\n\nNo degree required\n\nPreferred Qualifications\nBackground as a Data Scientist, Data Analyst, or Analytics Engineer\n\nExperience reviewing, auditing, or red-teaming someone else's analysis or model output\n\nFamiliarity with common pitfalls in applied statistics: p-hacking, leakage, confounding, multiple comparisons\n\nFamiliarity with cloud platforms (AWS, GCP, or Azure), data warehouses (Snowflake, BigQuery, Redshift), and MLOps practices such as experiment tracking and model versioning\n\nExperience with Git and collaborative, version-controlled workflows\n\nPrior experience with AI/ML data annotation, evaluation, or model-training projects\n\nWhat We Look For\nSkepticism and curiosity: you ask whether a result is real before you ask whether it is exciting\n\nHonesty about uncertainty: you communicate what the data can and cannot support\n\nStrong written communication and attention to detail\n\nSelf-direction: you are comfortable working independently and asynchronously in a remote environment\n\nWhat We Offer\nCompetitive salary and performance-based bonuses\n\nFlexible remote work environment\n\nProfessional growth opportunities and mentorship\n\nEngaging and collaborative team culture with cutting-edge projects\n\nReady to Make an Impact?\nIf you're passionate about rigorous data work and want to help shape how the next generation of AI models reason about data, we'd love to hear from you - send your resume to 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