ML Scientist: Climate & Energy AI Impact (Remote)

POWER USA; Northern (KY); Hybride; USD 160 000 - 190 000; Plein temps

Avantages offerts par ce poste

Medical insurance

Remote work

401(k) with match

Education reimbursement

Résumé du poste

GoodPower seeks a Machine Learning Scientist to expand what our teams and software can accomplish through applied research, predictive modeling, and intelligent automation across clean energy planning, public participation, media intelligence, and organizational effectiveness.

You will own the scientific development of ML capabilities from problem framing and experimentation through validation and deployment with engineering partners, delivering better decisions, more useful products, and

Qualifications

• PhD or equivalent in ML/CS with applied research experience.
• Proven ability to turn ambiguous problems into rigorous experiments and produce actionable AI/ML capabilities.
• Hands-on experience with NLP, embeddings, retrieval-augmented generation, and systematic evaluation.

Responsabilités

• Translate user needs and strategic challenges into tractable ML problems and establish baselines.
• Develop models for classification, entity resolution, anomaly detection, forecasting, and risk assessment across diverse data sources.
• Select and test ML methods from classical models to deep learning and graph methods, evaluating value added by complexity.
• Build retrieval and synthesis systems turning documents and data into verifiable answers.

Connaissances

NLP & LLMs

Python

PyTorch

TensorFlow

Data modelling

Formation

PhD in ML/CS

Outils

PyTorch

TensorFlow

scikit-learn

Pandas

SQL

Description du poste

GoodPower seeks a Machine Learning Scientist to expand what our teams and software can accomplish through applied research, predictive modeling, and intelligent automation across clean energy planning, public participation, media intelligence, and organizational effectiveness.

You will own the scientific development of ML capabilities from problem framing and experimentation through validation and deployment with engineering partners, delivering better decisions, more useful products, and