Required to have knowledge of statistical techniques, hypothesis testing, and experimental design for healthcare research.
Strength in APIs, distributed systems, and ML Ops (K8s, CI/CD, monitoring).
Required to have solid understanding of data engineering principles, data structures, and algorithms. Proficient in Python and/or other programming languages commonly used in ML development.
5+ years building production software/ML systems, including 1+ years of experience with LLMs/GenAI.
Required to have experience with Java or Python, Angular, React, JSON, Application Servers, CI/CD is preferred.
Experience with ML platforms and ML Ops: Demonstrated experience in assessing and improving ML platforms, identifying gaps, and architecting solutions to address them. Strong familiarity with ML platform components such as data ingestion, preprocessing, feature stores, model training, deployment, and monitoring.
Demonstrated delivery of RAG, prompt engineering, evaluation frameworks, and guardrails in production.
Proficient in Python and one major DL/LLM stack (e.g., PyTorch/Transformers); experience with LangChain/LlamaIndex, vector DBs, and cloud (AWS/Azure/GCP).
Experience with Agile/Scrum methodology and best practices
Required to have 5+ years of experience as a Data Scientist with a strong focus on Azure and Microsoft Data Science, AI, and machine learning toolsets.
Experience with SQL and big data platforms such as Postgres, Redshift and Snowflake
Required to have experience with one or more AI automations platforms like Kubeflow pipeline, MLFlow, Azure Pipeline, AWS Sage Maker Pipeline, Airflow, Jenkins, Spark, Hadoop, Kafka, Jira and GIT.
Required to have s trong machine learning expertise: Proficient in machine learning algorithms, statistical modeling, and data analysis. Hands-on experience with standard ML frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., scikit-learn, XGBoost, TensorFlow, or Keras).
Required to have strong problem-solving skills and the ability to tackle complex healthcare challenges using data-driven approaches. Can help the Data Science infrastructure building up, working with ML Ops team for model implementation, mentoring and developing junior staff.
Required to have f amiliarity with healthcare-related datasets, medical terminologies, and electronic health records (EHR) data.
Required to have s trong proficiency in data analysis, data manipulation, and data visualization using Python.
Bachelor's Degree (Required)