{"id":3649200,"url":"https://alion.io/job/latino-legends-data-scientist","title":"Data Scientist","company":{"id":4494772,"name":"Latino Legends","domain":"latinolegends.com","url":"https://alion.io/company/latino-legends","size_band":null,"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":"contractor","work_mode":"remote","remote_scope":"stated_regions","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["AR","BR","CO","MX","AI","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"],"hiring_countries_total":48,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Claude","optional":false},{"name":"Copilot","optional":false},{"name":"Cursor","optional":false},{"name":"Databricks","optional":false},{"name":"Git","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Scrum","optional":false},{"name":"SQL","optional":false},{"name":"Azure","optional":true},{"name":"Azure DevOps","optional":true},{"name":"CI/CD","optional":true},{"name":"Delta Lake","optional":true},{"name":"GitHub Actions","optional":true},{"name":"MLFlow","optional":true},{"name":"Plotly","optional":true},{"name":"Power BI","optional":true},{"name":"Spark","optional":true},{"name":"Tableau","optional":true},{"name":"Time Series","optional":true}],"status":"live","first_seen_at":"2026-11-01T12:33:46Z","employer_posted_date":null,"last_verified_at":"2026-11-01T12:33:46Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"Data Scientist\n(Machine Learning & Databricks)\n💰Salary | $2,000 USD/month (Bi-weekly payments)\n📍 Location | Remote from Latin America\n🕙 Schedule | Full-time\n💼 Employment Type | Contractor\n🇺🇸 Perks & Time Off | U.S. holidays off, generous PTO, internal training, and mentorship\nAbout the ClientLatino Legends is partnering with a high-growth U.S. marketing intelligence company to find top-tier nearshore talent. Our client does not just report on data—they build with it, driving marketing decisions for over 1,000 dealer clients across digital and direct channels. They operate on a cloud-native Databricks Lakehouse platform and foster an AI-forward culture where modern tooling accelerates analysis and deployment.\nThe RoleAs a Data Scientist, you will work primarily inside Databricks building datasets, features, and machine learning models that power marketing intelligence. You will spend your time on data preparation, exploratory analysis, feature engineering, and model training/evaluation, supported by senior engineers and architects on the production side.\nKey ResponsibilitiesData & Modeling (80%)\nBuild and maintain data pipelines and transformations in Databricks using Python, PySpark, and SQL.\n\nPerform exploratory data analysis to understand marketing, campaign, and customer datasets.\n\nEngineer features, prepare training datasets, and train, evaluate, and tune machine learning models.\n\nSupport model deployment and monitoring in production alongside senior engineers.\n\nInvestigate and resolve data quality issues in source feeds and pipelines.\n\nLeverage AI coding tools efficiently while maintaining code quality and deep understanding.\n\nCollaboration & Quality (20%)\nParticipate in sprint planning, standups, and retrospectives within an Agile/SCRUM framework.\n\nWrite tests, data validation checks, and participate in code reviews.\n\nDocument datasets, features, model assumptions, and results.\n\nPresent findings and recommendations to both technical and business stakeholders.\n\nCore QualificationsRequired:\nExperience: 1-3 years of hands-on experience in data science, machine learning, or analytics engineering (including internships, co-ops, research, or complex projects).\n\nEducation: Degree in Computer Science, Statistics, Mathematics, Engineering, Data Science, or comparable practical experience.\n\nLanguages & Core Tooling: Solid proficiency with Python (pandas, NumPy, scikit-learn) and SQL (joins, aggregations, window functions).\n\nData Science Fundamentals: Solid understanding of supervised learning, train/test splits, feature engineering, cross-validation, and evaluation metrics (ROC AUC, RMSE, precision, recall).\n\nStatistics & Data Handling: Working knowledge of descriptive statistics, hypothesis testing, data profiling, and data cleaning.\n\nModern Workflow: Proficiency with Git, collaborative development, and AI-assisted development tools (Cursor, GitHub Copilot, Claude, etc.).\n\nCommunication: Strong written and spoken English skills to effectively explain complex findings to non-technical audiences.\n\nNice to Have:\nHands-on experience with Databricks (Unity Catalog, clusters, jobs, notebooks) or PySpark.\n\nExposure to MLflow, Delta Lake (medallion architecture), or Azure cloud services.\n\nExperience with time-series forecasting, uplift modeling, or audience segmentation.\n\nFamiliarity with visualization tools (Power BI, Tableau, Plotly) or CI/CD pipelines (GitHub Actions, Azure DevOps).\n\nInterest in Large Language Models (LLMs) and generative AI applications.\n\nWhy Apply Through Latino Legends?\nModern Stack: Hands-on experience in Databricks on a modern lakehouse platform with zero legacy code clutter.\n\nMentorship & Career Growth: Work side-by-side with senior architects who review your code and help you transition from notebook experiments to production-level machine learning.\n\nHigh Impact: Your models directly inform millions of dollars in active marketing spend.\n\nDistributed Culture: Enjoy a collaborative, cross-border culture across the U.S. and Latin America.\n\nOriginally posted on Himalayas","description_format":"text","description_chars":4057,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"Argentina","iso":"AR","kind":"region"},{"name":"Brazil","iso":"BR","kind":"region"},{"name":"Colombia","iso":"CO","kind":"region"},{"name":"Mexico","iso":"MX","kind":"region"},{"name":"Anguilla","iso":"AI","kind":"region"},{"name":"Antigua and 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