Sr. Data Scientist
Role details
Job location
Tech stack
Job description
· AI and Machine Learning: Design, build, and deploy predictive, generative, and agentic AI models for healthcare applications. · Data Analysis & Insights: Perform advanced analytics and exploratory data analysis (EDA) on structured and unstructured healthcare datasets, including EHR, claims, and demographic data, ensuring HIPAA compliance. · Collaboration: Work closely with IT and business teams to identify use cases, define requirements, and deliver impactful solutions. · Innovation Leadership: Lead machine learning projects across healthcare, GIS, and logistics domains; champion new methodologies and best practices. · Data Science Subject Matter Expertise: As a senior Data Scientist, lead and drive methodologies and approaches to solving complex problems · End-to-End Delivery (Full stack): Manage full lifecycle of AI solutions-from prototyping to production deployment and system integration. · Model Monitoring: Deploy, monitor, and optimize traditional ML, Generative AI, and Agentic models for continuous improvement. · Stakeholder Engagement: Partner with business leaders to translate complex data into actionable insights and recommendations.
Requirements
· Education: Master's or Ph.D. in Data Science, Computer Science, Statistics, or related field. · Experience: 6-8 years of hands-on experience in applied machine learning, predictive modeling, and generative AI, preferably working with clinical (structured & unstructured), Geographic Information System (GIS) datasets. Technical Skills · Strong Proficiency in Python, R, and Java; strong SQL skills. · Expertise in ML frameworks (TensorFlow, PyTorch) and data manipulation libraries (pandas, dplyr). · Experience with data visualization tools (e.g. Streamlit/Dash, matplotlib, ggplot2, Plotly). · Familiarity with Generative AI techniques, RAG pipelines, and agentic frameworks like LangChain or LlamaIndex. · Knowledge of cloud-based AI/ML platforms (Azure, Databricks or equivalent). Statistical & ML Methodologies · Strong foundation in statistical modeling, hypothesis testing, regression analysis, time-series forecasting, and Bayesian methods. · Experience with supervised and unsupervised learning, ensemble methods, deep learning architectures, process mining and NLP techniques. · Understanding of model evaluation metrics, bias/variance trade-offs, and techniques for interpretability and fairness. Soft Skills · Excellent communication and ability to simplify complex concepts for diverse stakeholders. Strong multitasking and project management skills. Team and Culture · Collaborative & Innovative: Work in a team that values creativity, learning, and impact. · Mission-Driven: Help us achieve our goal of driving 90% of EMS requests through digital platforms and delivering new services to customers.
Benefits & conditions
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