Data Science Intern (Summer/Fall 2025) [US Only]
About Grantx
Grantx is building the operating system that helps startups, municipalities and research teams find, win, and manage non-dilutive funding. Our AI-driven platform surfaces the right opportunities in seconds, predicts win-probability, and automates post-award compliance. We’re a seed-stage GovTech/FinTech company backed by domain-expert angels, headquartered in Portland, ME with a distributed team across North America.
The Role
You’ll join our Data pod (Marco + Venky) to improve the quality, coverage and reliability of the world’s largest structured database of public- and private-sector grants. You’ll ship production code, not slide decks; your analysis will surface directly in our product and dashboards. Expect fast iterations, measurable impact, and mentorship from engineers who care about craft.
What You’ll Do
• Design and run experiments to raise deterministic-filter coverage for critical grant fields (award ceiling, close date, funder id)
• Prototype heuristics and ML classifiers that detect cyclical grants and flag conflicting data during ingestion
• Build validation pipelines in Python (Pandas, Polars) and orchestrate them in our Dagster environment
• Create insight reports on NOFO (Notice of Funding Opportunity) prevalence across grant types to guide roadmap decisions
• Present findings in concise, KPI-anchored reviews to the COO and product leadership
You Might Be a Fit If
• You’re pursuing (or recently completed) a B.S./M.S. in Data Science, Statistics, Computer Science, or related field
• You’ve completed at least one significant project using Python data-stack (Pandas, NumPy, scikit-learn or similar)
• You can write clean SQL and are comfortable reasoning about schema design and data lineage
• You enjoy turning messy, real-world datasets into actionable metrics and can explain your approach in plain language
• You thrive in fast-moving, ambiguity-heavy environments and value shipping iterations over chasing perfection
Bonus Points
• Experience with Dagster, Airflow, or other data-orchestration tools
• Familiarity with document understanding models (LayoutLM, DiT) or language-model evaluation
• Prior work involving government or financial datasets
What We Offer
• Competitive hourly rate and optional equity stipend for longer engagements
• Fully remote culture with quarterly in-person meetups (travel covered)
• Dedicated mentor and weekly learning budget (books, courses, conferences)
• Opportunity to convert to full-time role as we scale
Apply Now
Apply Now
Grantx is building the operating system that helps startups, municipalities and research teams find, win, and manage non-dilutive funding. Our AI-driven platform surfaces the right opportunities in seconds, predicts win-probability, and automates post-award compliance. We’re a seed-stage GovTech/FinTech company backed by domain-expert angels, headquartered in Portland, ME with a distributed team across North America.
The Role
You’ll join our Data pod (Marco + Venky) to improve the quality, coverage and reliability of the world’s largest structured database of public- and private-sector grants. You’ll ship production code, not slide decks; your analysis will surface directly in our product and dashboards. Expect fast iterations, measurable impact, and mentorship from engineers who care about craft.
What You’ll Do
• Design and run experiments to raise deterministic-filter coverage for critical grant fields (award ceiling, close date, funder id)
• Prototype heuristics and ML classifiers that detect cyclical grants and flag conflicting data during ingestion
• Build validation pipelines in Python (Pandas, Polars) and orchestrate them in our Dagster environment
• Create insight reports on NOFO (Notice of Funding Opportunity) prevalence across grant types to guide roadmap decisions
• Present findings in concise, KPI-anchored reviews to the COO and product leadership
You Might Be a Fit If
• You’re pursuing (or recently completed) a B.S./M.S. in Data Science, Statistics, Computer Science, or related field
• You’ve completed at least one significant project using Python data-stack (Pandas, NumPy, scikit-learn or similar)
• You can write clean SQL and are comfortable reasoning about schema design and data lineage
• You enjoy turning messy, real-world datasets into actionable metrics and can explain your approach in plain language
• You thrive in fast-moving, ambiguity-heavy environments and value shipping iterations over chasing perfection
Bonus Points
• Experience with Dagster, Airflow, or other data-orchestration tools
• Familiarity with document understanding models (LayoutLM, DiT) or language-model evaluation
• Prior work involving government or financial datasets
What We Offer
• Competitive hourly rate and optional equity stipend for longer engagements
• Fully remote culture with quarterly in-person meetups (travel covered)
• Dedicated mentor and weekly learning budget (books, courses, conferences)
• Opportunity to convert to full-time role as we scale
Apply Now
Apply Now