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Automatically closed by a robot after apply link was detected as broken.The candidate must work with key stakeholders within Research and Development and Operations to assess the potential value and risks of business problems solvable by machine learning and AI.
They should develop exploratory data analysis approaches to verify initial hypotheses for AI/ML use cases.
Documenting approaches, thinking, and results is essential to facilitate collaboration with other data scientists.
The candidate must prepare final trained models and develop validation test sets for quality assurance.
They will deploy models into production and support the monitoring of model performance.
Collaboration with other data science teams to support their projects and contribute to knowledge sharing is required.
Participation in design sessions to continuously develop and improve the machine learning platform is expected.
An advanced degree in Computer Science, Engineering, Statistics, or a related quantitative field is required.
The candidate should have 5+ years of hands-on data science experience with tools such as pandas, scikit-learn, keras, nltk, TensorFlow/PyTorch.
Proven experience in NLP, using transformers, fine-tuning LLMs, and deploying models with tools like Hugging Face is necessary.
Experience in building production-grade machine learning deployments on platforms like AWS, Azure, or GCP is required.
The candidate should have experience working with Apache Spark™ and large-scale distributed datasets.