Senior Data Scientist

Numeral • San Francisco, CA

50d

About The Position

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.

Requirements

• 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.

Nice To Haves

• 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.

Responsibilities

• 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 This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed