Senior Data Scientist + Machine Learning Engineer

Neo.Tax•Palo Alto, CA

1d•Remote

About The Position

Neo.Tax is automating the calculation of R&D tax credits and software capitalization, processes that currently cost enterprises millions and take months of manual work. Our software ingests data from various systems and uses ML/LLMs to perform these tasks in hours. We are seeking a Senior Data Scientist + Machine Learning Engineer to build and deploy ML models and production ML systems that enhance our core product experiences and automate complex tax and accounting workflows. This is a hands-on, product-focused role where you will tackle ambiguous problems, define measurable objectives, develop robust solutions, and collaborate with engineering and product teams for production deployment and iteration. While we are a remote company, we prefer candidates who can overlap with our San Francisco, CA headquarters.

Requirements

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).
Ability to operate with high ownership in ambiguous environments; strong communication and collaboration skills.
Ability to effectively design and implement solutions without the help of AI.
Experience with LLM evaluation, synthetic data generation, RAG, or tool-augmented agents.

Nice To Haves

Experience with information extraction and document understanding.
Experience with distributed data processing (e.g., Spark, Beam) and/or workflow engines.
Experience with GCP, AWS, or Azure.
Experience working at early-stage, venture-backed startups.

Responsibilities

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.
LLM systems: Design and evaluate prompt + retrieval + tool-calling pipelines; improve quality through datasets, labeling, and systematic evaluation.
Data foundations: Define datasets, labeling strategies, and data quality checks; build features that generalize across customer contexts.
Experimentation and evaluation: Design offline evaluations and online experiments; build dashboards and monitoring to detect regressions.
Production ML engineering: Build and operate training/inference pipelines (batch and/or online), model serving, feature/data pipelines, and monitoring/alerting for quality, latency, and cost.
Partner with engineering: Collaborate on productionization, scalability, reliability, latency, and cost; contribute directly to model-serving or batch pipelines as needed.
Cross-functional collaboration: Work with product, engineering, and customer-facing teams to understand workflows and translate real customer pain into ML deliverables.
Technical communication: Write clear specs and postmortems, document trade-offs, and communicate progress, risks, and decisions.