Senior Data Scientist + Machine Learning Engineer

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.

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).

Benefits

• Salary range: $190,000-210,000
• Stock Option Plan (Equity)
• Health Care Plans (Medical, Dental, Vision, Short-term Disability)
• 90% coverage for individual + family
• Health & Wellness subsidy
• Retail Plan (401k)
• Paid Time Off (Vacation, Sick & Public Holidays)
• Family Leave (Maternity, Paternity)
• Work From Home option