Forward Deployed Data Scientist - Oil & Gas, Houston

About Fundamental

Fundamental is an AI research lab pioneering the future of enterprise decision-making. Our flagship model, NEXUS is the world's most powerful Large Tabular Model (LTM) - purpose-built for the structured records that contain trillions of dollars in business value. With $275m in funding from leading investors and trusted by Fortune 100 companies, Fundamental is giving businesses the Power to Predict.

At Fundamental, you'll work on unprecedented technical challenges in foundation model development and build technology that transforms how the world's largest companies make decisions. This is your opportunity to be part of a category-defining company from the ground-up. Join the team defining the future of enterprise AI.

About the role

Fundamental is seeking a Forward Deployed Data Scientist - Oil & Gas, Houston to facilitate the adoption of NEXUS and collaborate with customers to address complex technical challenges with an initial focus on Houston-based Oil & Gas customers.

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.

In this role, you’ll manage customer relations involving multiple stakeholders (IT, C-suite, and data science teams) and function as a key bridge, translating field insights into our product roadmap.

Key responsibilities

You'll individually help deploy into production use cases with considerable business impact, moving from "science experiments" to definitive ROI

You’ll work on rigorous head-to-head benchmarking against client baselines (XGBoost, LightGBM), executing the work of data engineering, feature engineering, and validation

You’ll work in collaboration with our research and product teams to translate operational pain points and data anomalies into essential inputs for the Fundamental roadmap

You’ll be involved in technical strategy to identify the right business problems, prevent data leakage, and handle the "last mile" integration (VPC, on-prem, air-gapped)

Your collaboration with the Sales and Solution Architect teams will help align diverse stakeholders and explain predictions to business users

Must have

You hold a PhD / master in CS / Math / Stats or you have equivalent deep statistical literacy

You have 7+ years as a technical individual contributor in Data Science, Machine Learning, Applied Science, or ML Engineering

You have strong experience working directly with customers or business stakeholders, ideally in complex enterprise or industrial environments

You have experience with containerization (Docker), orchestration, and writing performant APIs (FastAPI/Flask)

You master the end-to-end pipeline, from framing, pre-processing, ml algorithm and validation strategies

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)

Prior experience or understanding of Oil & Gas, energy, utilities, industrials, logistics, or heavy asset environments

Experience as a Forward Deployed Engineer, Staff Engineer, Machine Learning Engineer, or Staff Data Scientist

Industry-based subject matter expertise

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

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

A mission-driven, low-ego culture that values diversity of thought, ownership, and bias toward action