Senior Data Scientist
2 months ago
As the Senior Data Scientist, you will work as part of a broader Data Science team in collaboration with Product and Engineering to deliver solutions focused on improving quality and efficiency of care delivery in the context of hospital-level care at home. You will play a crucial role in improving healthcare delivery and patient care by leveraging data science.
What you’ll do
- Lead data-driven decision-making by identifying key insights from large and complex datasets, driving strategic business initiatives.
- Develop and implement end-to-end machine learning pipelines, including data preprocessing, feature engineering, model training, and deployment.
- Design, develop, and deploy cutting-edge NLP models for tasks such as text classification, sentiment analysis, and named entity recognition.
- Lead the research and application of advanced NLP techniques, including transformers, BERT, GPT, and other deep learning frameworks.
- Collaborate with product managers and engineers to translate business requirements into scalable technical solutions.
- Optimize and fine-tune NLP models for performance, accuracy, and scalability in production environments.
- Mentor and guide junior data scientists, promoting a culture of innovation and continuous learning.
- Bachelor’s degree in Computer Science, Data Science, Statistics, or a related field.
- 3+ years of hands-on experience in data science or machine learning, with a focus on NLP.
- Master’s or Ph.D. in Computer Science, Data Science, Statistics, or a related field.
- Extensive experience with large-scale NLP models and frameworks (e.g., BERT, GPT-3/4, T5).
- Proficiency in fine-tuning and optimizing pre-trained language models.
- Experience with transfer learning and domain adaptation for NLP tasks.
- Strong programming skills in Python, with expertise in NLP libraries such as spaCy, Hugging Face, or NLTK.
- Familiarity with deep learning frameworks like TensorFlow or PyTorch.
- Experience deploying machine learning models on cloud platforms (e.g., AWS, GCP, Azure).
- Knowledge of containerization tools like Docker and orchestration with Kubernetes.
- Solid understanding of MLOps practices for continuous integration, deployment, and monitoring of ML models.
- Familiarity with tools like MLflow, Kubeflow, or similar platforms.
- Prior experience in leading or mentoring a team of data scientists or machine learning engineers.
- Proven ability to manage multiple projects and deliver results in a fast-paced environment.
- Knowledge of advanced NLP techniques such as reinforcement learning, conversational AI, or graph-based models.
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