Senior Data Scientist – Machine Learning Engineer

Job not on LinkedIn

🕒 July 28

🏄 California – Remote

🏄 California – Remote

💵 $190k - $210k / year

⏰ Full Time

🟠 Senior

📊 Data Scientist

👻 Ghost score 6%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Neo.Tax

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2019

🤖 Artificial Intelligence

☁️ SaaS

🏢 Enterprise

💰 Series A - Neo.Tax on 2023-07

Artificial Intelligence • SaaS • Enterprise

Neo. Tax is an AI-driven enterprise software company that automates R&D tax credit identification, calculation, and documentation and handles ASC 350-40 software capitalization. Neo. Tax ingests engineering and finance signals (Jira, GitHub, payroll, ledgers, tickets, org charts, etc. ), runs agentic AI models trained on IRS codes, regs and case law to qualify projects, quantify QREs, generate audit-ready studies and supporting documentation, and deliver real-time credit calculations. The product is delivered as a secure, SOC 2 Type II-capable SaaS with single-tenant and customer-hosted options and is targeted at large enterprise tax teams, CFOs, and auditors to reduce interviews, external advisor costs, and audit risk.

📋 Description

• 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. • Experience with LLM evaluation, synthetic data generation, RAG, or tool-augmented agents. • Bonus: Experience with information extraction and document understanding. • Bonus: Experience with distributed data processing (e.g., Spark, Beam) and/or workflow engines. • Bonus: Experience with GCP, AWS, or Azure. • Bonus: Experience working at early-stage, venture-backed startups.

🏖️ 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 • Retirement Plan (401k) • Paid Time Off (Vacation, Sick & Public Holidays) • Family Leave (Maternity, Paternity) • Work From Home option