Senior Data Scientist

The Nuclear Company is the fastest growing startup in the nuclear and energy space creating a never before seen fleet‑scale approach to building nuclear reactors. Through its design‑once, build‑many approach and coalition building across communities, regulators, and financial stakeholders, The Nuclear Company is committed to delivering safe and reliable electricity at the lowest cost, while catalyzing the nuclear industry toward rapid development in America and globally.

We’re seeking a Senior Data Scientist to join our Nuclear OS team and build the AI/ML capabilities that will transform nuclear construction. This senior‑level position offers the unique opportunity to develop predictive analytics and machine learning models that learn from historical and real‑time data to predict future outcomes, detect anomalies, and optimize nuclear project delivery. You’ll work with cutting‑edge AI/ML technologies and data integration platforms, deploying models that directly impact the efficiency, safety, and cost‑effectiveness of fleet‑scale nuclear deployment.

• Predictive Analytics Development:
  Host and develop various ML models that learn from historical and real‑time data to predict future outcomes or detect anomalies, including schedule slippage prediction, equipment failure forecasting, and risk assessment.
• AI Model Development & Deployment:
  Develop, train, and deploy machine learning models within the Palantir Foundry environment, managing model development, training, and inference at scale while ensuring models operate on governed, quality‑controlled data.
• Anomaly Detection & Optimization:
  Introduce ML algorithms on numeric datasets to identify outliers or predict issues, applying time‑series anomaly detection to identify unusual fluctuations that could indicate problems.
• Algorithm Optimization:
  Fine‑tune and optimize predictive analytics algorithms to improve the accuracy of fault detection and predictive maintenance, adjusting machine learning models based on historical data to enhance prediction accuracy.
• AI‑Driven Decision Support:
  Build AI capabilities that augment human decision‑making with AI intelligence, helping project managers identify potential risks before they become problems and enabling real‑time adaptation to prevent cost overruns.
• Data Analysis & Visualization:
  Perform comprehensive data analysis and create visualizations that communicate insights to stakeholders, enabling data‑driven strategy and decision‑making.
• Model Training & Continuous Improvement:
  Train models on historical data from previous construction projects and continuously improve them with incoming data from ongoing projects, scheduling retraining as new data accumulates.
• Cross‑Project Learning:
  Develop fleet‑wide learning capabilities where data from each project is aggregated to enable performance benchmarking and cross‑project AI, creating a self‑improving fleet deployment model.
• AI/ML Infrastructure:
  Build and maintain pipelines for training and running models at scale using Python ML libraries (Tensor Flow/PyTorch), ensuring auditability and transparency of outputs.
• Simulation & What‑If Analysis:
  Develop simulation engines and what‑if analysis tools to simulate schedule adjustments or supply chain disruptions using unified data from the ontology.
• Collaboration & Communication:
  Work closely with engineering, construction, and operations teams to understand business problems, translate them into data science solutions, and communicate findings to both technical and non‑technical stakeholders.

• 7‑12 years of experience in data science, machine learning, or advanced analytics.
• Proven track record of developing and deploying production ML models at scale.
• Experience with predictive analytics, anomaly detection, and optimization algorithms.

• Machine Learning:
  Deep expertise in AI/ML systems, including supervised and unsupervised learning, time‑series analysis, and anomaly detection.
• Programming:
  Advanced proficiency in Python and ML libraries (Tensor Flow, PyTorch, scikit‑learn) for model development and deployment.
• Data Platforms:
  Familiarity with machine…