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Fundamental

Predictive analytics platform for structured data

Forward Deployed Data Scientist

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeRemote in USARemote

About the Job

Requirements

  • Hold a PhD or master's degree in Computer Science, Mathematics, or Statistics, or have equivalent deep statistical literacy.
  • Have 2+ years of experience as a technical individual contributor, such as a data scientist or software engineer.
  • Have experience with containerization using Docker, orchestration, and writing performant APIs with FastAPI or Flask.
  • Understand the end-to-end machine learning pipeline, including problem framing, preprocessing, machine learning algorithms, and validation strategies.
  • Have a deep understanding of data handling with PySpark and Pandas, and memory optimization.
  • Have demonstrated experience optimizing models for a specific business problem.
  • Have strong communication skills and be able to translate architectural nuances into clear business value.

Responsibilities

  • Individually help deploy production use cases with considerable business impact, moving from science experiments to measurable return on investment.
  • Conduct rigorous head-to-head benchmarking against client baselines such as XGBoost and LightGBM, carrying out data engineering, feature engineering, and validation.
  • Collaborate with research and product teams to translate operational pain points and data anomalies into inputs for the product roadmap.
  • Contribute to technical strategy by identifying suitable business problems, preventing data leakage, and handling last-mile integration, including virtual private cloud, on-premises, and air-gapped environments.
  • Collaborate with Sales and Solution Architect teams to align stakeholders and explain predictions to business users.
  • Manage customer relationships involving multiple stakeholders, including IT, C-suite, and data science teams, and translate field insights into the product roadmap.

Desired Qualifications

  • Experience with PyTorch and cloud-native machine learning pipelines using AWS, GCP, or Azure.
  • Experience as a Forward Deployed Engineer, Staff Engineer, Machine Learning Engineer, or Staff Data Scientist.
  • Industry-based subject matter expertise.

About the Company

Fundamental

Predictive analytics platform for structured data

Fundamental offers an enterprise-grade predictive intelligence product called NEXUS, a Large Tabular Model that analyzes structured data from spreadsheets, databases, and CRM systems. It learns patterns directly from raw tabular data, eliminating the need for extensive feature engineering, and combines forecasting, classification, anomaly detection, and scenario simulation in one model. NEXUS is deployed through the AWS dashboard via a strategic partnership and was trained on Amazon SageMaker HyperPod to handle datasets with billions of rows, serving industries like financial services, healthcare, retail, and energy with seven-figure contracts for use cases like demand forecasting, price prediction, and churn analysis. The goal is to turn raw data into actionable insights that improve forecasting accuracy, reduce risk, and optimize business outcomes, setting Fundamental apart by focusing on structured data rather than unstructured text or images.

Company size
51-200
Company stage
N/A
Total funding
$255M
Headquarters
San Francisco, CA
Founded
2024

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