{"id":1536757,"url":"https://alion.io/job/ramp-applied-scientist-intern","title":"Applied Scientist Intern","company":{"id":4342,"name":"Ramp","domain":"ramp.com","url":"https://alion.io/company/ramp","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":88,"open_postings":44,"ghost_share":0,"stale_share":0.5,"repost_share":0,"time_to_fill_p50_days":58,"computed_at":"2026-10-23T05:54:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"intern","employment_type":"internship","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["New York, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":12500,"max":12500,"currency":"USD","period":"month","gross":null,"usd_annual":150000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"ClickHouse","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Dagster","optional":true},{"name":"Git","optional":true},{"name":"Metaflow","optional":true},{"name":"Prefect","optional":true}],"status":"live","first_seen_at":"2026-09-15T01:44:04Z","employer_posted_date":"2026-09-15","last_verified_at":"2026-10-23T22:45:44Z","board_verified":true,"closed_at":null,"days_open":38,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":38},"description":"About Ramp\nRamp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $275B in annualized spend flows in and out of 85,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books.\nThe problems are high-stakes, data-dense, and unforgiving.\nWe hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome.\nThe median Ramp customer saves 7.5% and grows revenue 16% in their first year - far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same.\nIf you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it.\nAbout the Role\nThe Applied Science team builds models and tools that solve Ramp’s most critical problems: from underwriting businesses to combatting fraud to making spend management smarter. We’re deeply embedded in the business and provide a quantitative foundation for decision making.\nAs an Applied Science intern, you’ll be a fully integrated member of the team and own your project from start to finish. Working with engineers, product managers, and business stakeholders, you’ll translate complex business needs into scalable machine-learning-driven solutions. This is a chance to apply ML concretely, ship code, and create genuine value for Ramp and our customers.\nYou will focus on exciting problems in areas like: credit, fraud, growth, or our core product.\nWhat You’ll Do\nEnd-to-End ML: own the model lifecycle from data exploration and feature engineering to training, benchmarking, deployment, and monitoring\n\nState-of-the-Art AI: leverage the latest Large Language Models (LLMs) to solve novel problems and create new product capabilities for our customers\n\nVersatile Techniques: apply the right tools to the right problems, whether it’s deep learning, gradient boosting, or causal inference\n\nRigorous Experimentation: quantify the impact of your work through A/B tests and other statistical methods\n\nCollaborate: partner closely with product and business leaders to translate models and insights into actionable strategy and user-facing features\n\nWhat You Need\nB.S., M.S. or Ph.D. Student: currently pursuing a degree in Data Science, Computer Science, Math, Physics, Economics, Statistics, or other quantitative fields with an expected graduation datebetween Dec 2027 - 2029. Graduate degrees are preferred, but not a must.\n\nStrong ML Fundamentals: solid understanding of the mathematical foundations of machine learning, statistics, probability, and optimization\n\nStrong Interest or Experience with AI: curiosity and drive to integrate cutting edge LLMs and agents into applied solutions\n\nPython Proficiency: good grasp of common Data Science libraries (pandas, scikit-learn, NumPy, PyTorch, etc.)\n\nSQL Knowledge: experience wrangling data in a modern data warehouse (e.g. Snowflake, BigQuery, Redshift, Clickhouse)\n\nPractical Experience: track record of curating datasets and building/evaluating ML models\n\nStrong Communication: ability to clearly explain complex concepts to both technical and non-technical audiences and use data to build a compelling narrative\n\nBias For Action: a comfort with ambiguity and desire to ship solutions quickly then iterate\n\nNice to Haves\nPublications, Projects, or Previous Experience: relevant experience applying AI/ML and demonstrating your passion for the field\n\nProduction ML Mindset: knowledge of software engineering best practices applied to ML including version control (Git), testing, and writing maintainable code\n\nData Orchestration: experience with leveraging modern data orchestration platforms (Airflow, Dagster, Prefect, Metaflow)\n\nCompensation\nThe monthly rate for this internship is $12,500 USD + housing stipend\n\nRamp Benefits\nApple MacBook\n\nCatered lunches in NYC office Monday-Friday\n\nWeekly coffee stipend\n\nBenefits available to all full-time Ramp employees (Global)\nFlexible PTO\n\nCentralized home-office equipment ordering\n\nHealth and wellness stipend\n\nBudget for intra-office travel\n\nWeekly coffee stipend\n\nUnited States\n100% medical, dental & vision insurance coverage for you, with partial coverage for dependents\n\nOne Medical annual membership\n\n401(k), including employer match on contributions made while employed by Ramp\n\nFertility HRA (up to $10,000 per year)\n\nParental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay\n\nPet insurance\n\nIn-office perks: lunch, snacks, drinks, and more\n\nRelocation expense coverage to NYC or SF (if needed)\n\nCanada\nGroup medical, dental, and vision coverage through Sun Life\n\nLife, AD&D, and disability coverage\n\nFertility drug coverage (up to $4,000 lifetime)\n\nGroup Retirement Plan with employer match (RRSP + DPSP)\n\nParental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay, with additional time available at reduced pay\n\nEmployee Assistance Program and virtual care through Lumino Health\n\nUnited Kingdom\nPrivate medical insurance through Freedom Elite\n\nVirtual GP and at-home care via eMed x Livi\n\nWorkplace pension through Penfold, with salary sacrifice option\n\nParental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay with additional time available at reduced pay\n\nReferral Instructions\nIf you are being referred for the role, please contact that person to apply on your behalf.\nOther notices\nPursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.\nBeware of recruiting scams: Ramp will only contact you through official @Ramp.com email addresses and will never ask for payment or sensitive personal information during the hiring process.\nRamp Applicant Privacy Notice","description_format":"text","description_chars":6006,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":false},"security_clearance":false,"languages":[]},"benefits":["Apple Macbook","Health insurance","Insurance coverage","Parental leave","Retirement plans","Vision insurance"],"hiring_locations":[{"name":"United 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