Senior Data Scientist – Machine Learning Engineer

Neo.Tax

Senior Data Scientist + Machine Learning Engineer at Neo.Tax, automating complex tax workflows using ML and data science. Build and ship models for core product experiences.

Posted 7/28/2026full-timeRemote • California • 🇺🇸 United StatesSenior💰 $190,000 - $210,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in ML/AI problem-solving, model development, and production ML engineering, with a strong foundation in statistical modeling and data pipeline construction. Proficient in collaborating cross-functionally to translate customer needs into effective ML solutions.

Highest-signal resume keywords

ML/AI Problem SolvingModel DevelopmentProduction ML EngineeringStatistical ModelingCross-Functional Collaboration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonNumPyPandasScikit-learnPyTorchTensorFlowSQLStatistical ModelingExperimentationData Pipeline Construction

Soft Skills

CommunicationCollaborationOwnership

Tools & Technologies

GCPAWSAzureSparkBeam

Certifications & Qualifications

MS/PhD in Computer ScienceStatisticsMathematics

Industry Keywords

Machine LearningArtificial IntelligenceData ScienceInformation ExtractionDocument Understanding

Tech Stack

Tools & technologies

AWSAzureGoogle Cloud PlatformNumpyPandasPythonPyTorchScikit-LearnSparkSQLTensorflow

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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