Senior Data Scientist
About Numeral
Numeral is building the automation backbone for internet commerce — starting with the painful world of sales tax compliance. We handle everything from registration to remittance, delivering a white-glove service so ecommerce and digital businesses can stay laser-focused on what they do best: growing their products, customers, and teams.
We’re one of the fastest-growing companies from Y Combinator’s W23 batch, backed by top-tier investors including Benchmark Capital, Mayfield and Uncork. Our team has deep roots from the early days at Stripe, Airbnb, Notion, and other breakout companies — and now we’re bringing that same level of craft, speed, and ambition to a space that’s long overdue for reinvention.
Numeral is small but mighty. Growth is already borderline unmanageable — which means every hire we make now will directly shape the trajectory of the company. If you’re excited about joining as an early team member and want the kind of ownership that defines careers, we want to meet you.
About the role
As a Sr. Data Scientist at Numeral, you’ll help build the intelligence layer that powers automated, accurate, and scalable compliance. You’ll work at the intersection of data, product, and engineering to develop models, analytics, and systems that improve accuracy, predict risk, and unlock automation across our platform.
This is not a research-only role. You’ll ship real production systems that directly impact customer outcomes, operational efficiency, and business growth.
This is a high-ownership role with broad scope – ideal for someone who thrives in fast-moving environments and wants their work to matter immediately.
What you’ll do
Build and deploy data models and algorithms that improve tax accuracy, anomaly detection, and operational efficiency.
Develop risk, quality, and confidence scoring across filings, transactions, and customer data.
Partner closely with Operations, Product and Engineering, to productionize models and integrate them into core workflows.
Design experiments and analyses to inform product decisions, automation strategies, and prioritization.
Identify patterns in large, messy, multi-source datasets (e.g., payments, e-commerce, filings, customer behavior).
Improve data reliability and observability across pipelines used by analytics, ops, and ML systems.
Collaborate with Data Analysts and Ops teams to translate operational pain points into scalable, data-driven solutions.
Help define Numeral’s long-term data science and ML roadmap.
What you’ll bring
5+ years of experience in Data Science, Applied ML, or advanced Analytics.
Strong proficiency in Python and SQL.
Experience building and shipping models or data products used in production.
Solid grounding in statistics, experimentation, and data modeling.
Ability to reason through ambiguity and design solutions from first principles.
Strong communication skills - able to explain complex ideas clearly to technical and non-technical partners.
Comfort working in a fast-paced startup environment with real ownership and minimal process.
Even better if you have
Experience with financial, transactional, payments, or compliance data.
Familiarity with Stripe, Shopify, Fivetran, Parquet, S3, DuckDB.
Experience with DBT or similar ETL frameworks.
Familiarity with ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow).
Exposure to anomaly detection, classification, forecasting, or risk modeling.
Prior startup or high-growth company experience.