Data Scientist

Job Title Title: Data Scientist
Location: Remote and Baltimore, MD
Duration: 9 months
Description:

• Formulate, design, and deliver AI/ML-based decision-making frameworks and models to achieve business outcomes.
• Develop and fine-tune AI models for NLP tasks, including Named Entity Recognition (NER), text classification, and sentiment analysis with a focus on unstructured clinical data.
• Analyze and preprocess large datasets, particularly unstructured medical records (e.g., physician notes, discharge summaries) using tools like Pandas, NLTK, and SpaCy.
• Create custom NLP algorithms and annotators for extracting insights from medical records.
• Work with human-in-the-loop systems, incorporating clinician feedback to refine and improve AI models.
• Conduct experiments to evaluate AI model performance, employing metrics such as precision, recall, and F1-score.
• Collaborate with cross-functional teams, including software engineers, to ensure AI solutions are integrated into the broader system architecture.
• Create custom tools to enable analysts to perform data research.
• Maintain comprehensive technical documentation and provide ongoing system support.
• Develop data pipelines for efficient data processing, integrating with databases such as SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Elasticsearch).
• Deploy AI models using cloud platforms (AWS, Azure) and containerization technologies like Docker, and manage them using CI/CD pipelines.

Requirements:

• 5+ years of experience in AI/ML development, with a strong focus on NLP using frameworks such as TensorFlow, PyTorch, and Hugging Face.
• Mastery in Python frameworks and libraries like Transformers, NLTK, SpaCy, Gensim, and data manipulation tools such as Pandas and NumPy.
• Proven experience with human-in-the-loop systems that incorporate clinician feedback to improve model performance.
• Experience with cloud platforms (AWS, Azure), containerization (Docker), and CI/CD pipelines for machine learning model deployment.
• Solid understanding of statistical modeling, data analysis, and performance evaluation metrics.
• Expertise in analyzing and processing unstructured clinical data using techniques such as tokenization, lemmatization, and word embeddings (e.g., TF-IDF, BERT).
• Knowledge of healthcare data formats and standards such as HL7, FHIR, ICD codes, and SNOMED.
• Ability to effectively articulate technical challenges and solutions, with excellent written and verbal communication skills.
• Familiarity with SQL and NoSQL databases and structuring data pipelines for efficient processing.
• Master s degree in Data Science, AI, Computer Science, or a related field + 10 years of experience; or PhD + 4 years of experience.

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