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We are seeking a visionary and technically accomplished Principal Data Scientist to lead advanced AI/ML initiatives across the organization. This role demands deep expertise in machine learning, generative AI, computer vision, and cloud-native AI solutions, with a strong emphasis on innovation, governance, and cross-functional collaboration
Explore the rewards and benefits that help you thrive - at every stage of your life and your career.
This includes:
Seize every opportunity to sharpen your skills, expand your expertise, and be recognized for the impact you make.
We're , a world-class engineering services and nuclear organization. We connect people, data and technology to transform the world's infrastructure and energy systems. Together, with our industry partners and clients, and our global team of consultants, designers, engineers and project managers, we can change the world. We're committed to leading our clients across our various end markets to engineer a better future for our planet and its people.
Job ID: 145362519
Skills:
Data Science, SAS, Matlab, Python, Sql, R
Skills:
statistical concepts , probability , Tableau, Sql, Clustering, Python, Sap Business Objects, R, R-Shiny, Microstrategy, Looker, regressions
Skills:
causal inference , feature stores, LLMs, representation learning, reinforcement learning, long-term optimization, semantic retrieval, sequential user modeling, Foundation Models, recommendation systems, experimentation frameworks, behavioral embeddings, vector retrieval, Transformers, multimodal AI, streaming pipelines, contextual bandits, online inference, model serving, search ranking, real-time ML systems
Skills:
causal inference , Scipy, Databricks, Clustering, Pandas, Regression Analysis, Numpy, Python, Hypothesis Testing, scikit-learn, statsmodels, statistical methodology, Bayesian Methods, Survival Analysis, Experimental Design, Simulation, time-series analysis
Skills:
unstructured data , Tensorflow, Nlp, Pytorch, Python, AWS, Testing, MLops, Keras, Azure, embeddings, knowledge graphs, vector databases, graph-based reasoning, structured data, model validation, model risk management, explainability, LLMs, LLMOps, Scikit-learn, Monitoring, prompt engineering, feature engineering, Responsible AI