Senior Data Scientist (Healthcare)

Kontakt.io is building the platform that care operations run on. We reduce waste, cut costs, and improve revenue by improving throughput, asset utilization and staff productivity. Our platform uses AI, RTLS, and EHR data to enable self-learning agents to automate workflows, adapt in real-time, and orchestrate all of care delivery operations. Easy to deploy and scale, it gives a clear picture of spaces, equipment, and people, eliminating inefficiencies and enhancing the patient experience. With measurable 10X ROI and over 20+ use cases, Kontakt.io is the go-to platform for better and faster care delivery operations. Our growth trajectory over the past 24 months has been exceptional: Team expansion from 70 to 130 professionals Quadrupled Annual Recurring Revenue (ARR) Maintained strong capital efficiency We are a fast-growing, mission-driven organization dedicated to transforming healthcare through data-driven insights. Our team harnesses Electronic Health Record (EHR) and Real-Time Location Systems (RTLS) data to deliver innovative analytics solutions that optimize clinical workflows, enhance patient outcomes, and streamline hospital operations. We are seeking a passionate and experienced Senior Data Scientist to help drive these initiatives and shape the future of healthcare. As a Senior Data Scientist, you will design and deploy advanced machine learning, operations research, and optimization solutions across various aspects of healthcare operations. Working alongside clinicians, data engineers, product managers, and hospital administrators, you will translate complex healthcare challenges into actionable strategies. The ideal candidate will have a strong technical skill set, proven leadership in end-to-end project delivery, and a deep understanding of healthcare data and regulations. You: Excel at developing and deploying machine learning models to drive improved patient care and operational efficiency using complex healthcare data. Are skilled at creating and scaling LLM-based solutions, including Retriever-Augmented Generation (RAG) architectures, for innovative applications in healthcare analytics. Bring expertise in applying operations research techniques such as linear programming, queueing theory, and optimization to healthcare challenges like scheduling, resource allocation, and capacity planning. Collaborate effectively with clinicians, operations teams, and product managers to align data-driven solutions with business goals. Design experiments, pilot studies, and A/B tests to assess the impact of interventions in clinical and operational contexts. Stay current with advancements in machine learning, operations research, and healthcare analytics, ensuring innovative and compliant solutions. Prioritize adherence to healthcare privacy regulations (e.g., HIPAA) and implement secure workflows for data handling and deployment. Have excellent communication skills and can convey complex technical concepts to both technical and non-technical stakeholders. Our requirements: 5+ years of experience in data science, predictive analytics, or related roles. Exposure to EHR data structures and workflows in healthcare. Proficiency in Python or R, with experience in libraries/frameworks like sci-kit-learn, PyTorch, or TensorFlow. Strong SQL skills and familiarity with distributed computing platforms like Spark or Hadoop. Experience with cloud environments (AWS, GCP, Azure) and MLOps best practices. Knowledge of LLMs, NLP, and Retriever-Augmented Generation (RAG). Strong analytical thinking and problem-solving abilities. Master's or PhD in Data Science, Computer Science, Statistics, Mathematics, Operations Research, or a related quantitative discipline. Nice to Haves: Hands-on experience with operations research methods, simulation modeling, and resource optimization in healthcare or related fields. Familiarity with RTLS data for asset tracking or workflow analysis. Previous experience mentoring or leading junior data scientists or engineers. Familiarity with DevOps/MLOps tools like Docker, Kubernetes, and Airflow, as well as CI/CD pipelines. Advanced knowledge of simulation modeling and its application in healthcare. Experience developing tools for real-time decision-making in healthcare environments. #J-18808-Ljbffr

Created: 2025-02-03