Data Scientist

Role Overview

Perform data and error analysis, clean and validate datasets, and build/deploy advanced machine learning and AI solutions. The role focuses on deriving insights from large datasets, creating predictive models, and improving healthcare-domain products and services.

Responsibilities

• Data analysis & interpretation: Extract insights from complex datasets, identify patterns, and support strategic decision-making.
• Machine learning model development: Design, develop, and train models using supervised/unsupervised learning, deep learning, and reinforcement learning.
• Agentic workflows: Implement AI agent workflows for autonomous task execution to improve efficiency and decision-making.
• RAG (Retrieval-Augmented Generation): Use RAG patterns so language models can access external knowledge and improve outputs.
• Model fine-tuning: Fine-tune pre-trained models for specific tasks/datasets.
• Data cleaning & preprocessing: Handle missing values, remove outliers, and ensure uniformity and accuracy.
• Deployment & monitoring: Deploy models to production, monitor performance, and adjust to maintain accuracy.
• Cross-functional collaboration: Work with software engineers, product managers, and business analysts to integrate solutions into systems.
• Research & development: Stay current with AI/ML advancements and apply them to existing and new methodologies.
• Documentation & reporting: Document models/results and communicate findings to non-technical stakeholders.

Requirements

• Education: Bachelor’s degree in Computer Science, Data Science, Statistics, or related field.
• Experience: 3+ years as a data scientist (healthcare preferred).
• Programming: Strong programming skills in Python and R.
• ML/AI frameworks: Experience with TensorFlow, Keras, or PyTorch.
• Statistics/ML: Strong understanding of statistical methods and algorithms including k-NN, Naive Bayes, SVM, and neural networks.
• Agentic workflows: Familiarity with designing/implementing agentic workflows using AI agents.
• RAG: Knowledge of retrieval-augmented generation techniques.
• Fine-tuning: Proven experience fine-tuning models for specific tasks and performance metrics.
• Data visualization: Proficiency with Tableau or Power BI.
• Databases & data pipelines: Experience with SQL/NoSQL, data warehousing, and ETL.
• Problem-solving: Strong analytical and problem-solving skills.

Nice to Haves

• Master’s degree in Computer Science (mentioned as preferred, but truncated in the posting).