Fundamental

Fundamental

Data Scientist (Forward Deployed)

US (remote) · Staff+

Sponsorship not specifiedDetected 36 days ago
FastAPIFlaskAWSGCPAzureDockerMachine LearningPyTorchPandasCommunication

About the role

  • The Data Scientist is an integral part of our FDE team, which is dedicated to driving the successful deployment of Fundamental products and proving value over legacy baselines or net new use cases.
  • They work hand-in-hand with customers from the Proof of Value stage to post-implementation, ensuring our solutions run securely in the client's production heartbeat.

Responsibilities

  • Relocation support for employees moving to join the team in one of our

Requirements

  • You hold a PhD / master in CS / Math / Stats or you have equivalent deep statistical literacy
  • You have 2+ years as a technical individual contributor (data scientist or software engineer)
  • You have experience with containerization (Docker), orchestration, and writing performant APIs (FastAPI/Flask)
  • You have a deep understanding of data handling (PySpark, Pandas) and memory optimization
  • You have demonstrated experience optimizing models for a specific business problem
  • You hold strong communication skills with an ability to translate architectural nuances into clear business value

Nice to have

  • Experience with PyTorch and cloud-native ML pipelines (AWS, GCP, Azure)
  • Industry-based subject matter expertise

Compensation

  • Competitive compensation with salary and equity

Benefits

  • Competitive compensation with salary and equity
  • Comprehensive health coverage for you and your dependents, including medical, dental, vision, and 401K
  • Paid parental leave for all new parents, inclusive of adoptive and surrogate journeys

Company info

  • Founded by DeepMind alumni, Fundamental has developed NEXUS - the world's most powerful Large Tabular Model (LTM) - purpose-built for the structured records that actually drive enterprise decisions.

This listing is sourced directly from Fundamental's careers page and normalized into a canonical job model.