Eliminate interviews and generate audit-ready R&D tax credit and software capitalization outputs in days, not months. Backed by GV.
About the role
Own ML/AI problem spaces end-to-end: Define success metrics, create baselines, iterate on approaches, and drive projects from prototype to production. Model development: Build and improve models spanning classification, information extraction, entity resolution, clustering, ranking, anomaly detection, and forecasting.
What they're looking for
- MS/PhD in Computer Science, Statistics, Mathematics, or a related quantitative field, or equivalent practical experience
- 6+ years of industry experience as a Data Scientist / Applied Scientist / ML Engineer shipping ML to production (or equivalent)
- Strong proficiency in Python and the modern data/ML ecosystem (NumPy/Pandas, scikit-learn, PyTorch or TensorFlow)
- Strong understanding of statistical modeling, experimentation, and evaluation (metrics, confidence intervals, A/B testing, bias/variance, error analysis)
- Experience building data pipelines and working with SQL and relational databases
- Experience deploying and maintaining models in production (batch or real-time), including monitoring and iteration, comfortable owning operational concerns (reliability, latency, cost)
More about this role
Enterprises waste millions on accounting firms to calculate R&D tax credits and capitalize software costs. Neo.Tax is automating this entirely. Our software ingests data from project management, identity management, payroll systems, and financial systems and uses ML/LLMs to do in hours what used to take months of manual work.
Neo.Tax is seeking a Senior Data Scientist + Machine Learning Engineer (combo role) to build and ship models and production ML systems that power our core product experiences and automate complex tax and accounting workflows. This role is hands-on and product-oriented: you will take ambiguous problems, turn them into measurable objectives, build robust solutions, and collaborate closely with engineering and product to deploy and iterate in production.
We are a remote company, but we prefer to hire in time zones that can overlap with our HQ in San Francisco, CA!
Own ML/AI problem spaces end-to-end: Define success metrics, create baselines, iterate on approaches, and drive projects from prototype to production.
Model development: Build and improve models spanning classification, information extraction, entity resolution, clustering, ranking, anomaly detection,...
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