Senior Data Scientist + Machine Learning Engineer

Neo.Tax•Palo Alto, CA

3d•$190,000 - $210,000•Remote

About The Position

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!

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 (more info on how we use AI at Neo.Tax below).
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.