Senior Data Scientist

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

1. Lead data-driven decision-making by identifying key insights from large and complex datasets, driving strategic business initiatives.
2. Develop and implement end-to-end machine learning pipelines, including data preprocessing, feature engineering, model training, and deployment.
3. Design, develop, and deploy cutting-edge NLP models for tasks such as text classification, sentiment analysis, and named entity recognition.
4. Lead the research and application of advanced NLP techniques, including transformers, BERT, GPT, and other deep learning frameworks.
5. Collaborate with product managers and engineers to translate business requirements into scalable technical solutions.
6. Optimize and fine-tune NLP models for performance, accuracy, and scalability in production environments.
7. Mentor and guide junior data scientists, promoting a culture of innovation and continuous learning.

Basic qualifications

1. Bachelor’s degree in Computer Science, Data Science, Statistics, or a related field.
2. 3+ years of hands-on experience in data science or machine learning, with a focus on NLP.

Preferred qualifications

1. Master’s or Ph.D. in Computer Science, Data Science, Statistics, or a related field.
2. Extensive experience with large-scale NLP models and frameworks (e.g., BERT, GPT-3/4, T5).
3. Proficiency in fine-tuning and optimizing pre-trained language models.
4. Experience with transfer learning and domain adaptation for NLP tasks.
5. Strong programming skills in Python, with expertise in NLP libraries such as spaCy, Hugging Face, or NLTK.
6. Familiarity with deep learning frameworks like TensorFlow or PyTorch.
7. Experience deploying machine learning models on cloud platforms (e.g., AWS, GCP, Azure).
8. Knowledge of containerization tools like Docker and orchestration with Kubernetes.
9. Solid understanding of MLOps practices for continuous integration, deployment, and monitoring of ML models.
10. Familiarity with tools like MLflow, Kubeflow, or similar platforms.
11. Prior experience in leading or mentoring a team of data scientists or machine learning engineers.
12. Proven ability to manage multiple projects and deliver results in a fast-paced environment.
13. Knowledge of advanced NLP techniques such as reinforcement learning, conversational AI, or graph-based models.

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