Senior Data Scientist

Experience

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

Technical Skills

Machine Learning: Deep expertise in AI/ML systems, including supervised and unsupervised learning, time-series analysis, and anomaly detection

Competitive compensation packages

401k with company match

Medical, dental, vision plans

Generous vacation policy, plus holidays

Estimated Starting Salary Range

The estimated starting salary range for this role is $150,000 - $173,000 annually less applicable withholdings and deductions, paid on a bi-weekly basis. The actual salary offered may vary based on relevant factors as determined in the Company’s discretion, which may include experience, qualifications, tenure, skill set, availability of qualified candidates, geographic location, certifications held, and other criteria deemed pertinent to the particular role.

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