Data Scientist Lead

About Kontakt.io http://Kontakt.io Every day, intelligent software orchestrates the physical world around us – from matching drivers with riders to optimizing global supply chains. Yet inside hospitals, where every second and every decision can affect a patient's life, operations are still spread across dozens of disconnected systems. At Kontakt.io http://Kontakt.io, we're changing that. By combining proprietary hardware, AI-powered intelligence, and deep integrations with the systems hospitals already rely on, we're creating a real-time understanding of hospital operations that software alone can't deliver. That intelligence powers the execution layer hospitals have been missing – helping care teams make smarter decisions and deliver better patient care. Backed by Goldman Sachs and trusted by leading health systems including HCA Healthcare, Sutter Health, AdventHealth, Trinity Health, Northwell Health, Cleveland Clinic, and the U.S. Department of Veterans Affairs, we're pioneering the next generation of healthcare operations. We've more than doubled our revenue over the past year and are on track to surpass $70M in annual recurring revenue – not because we're following a market, but because we're defining one. If you're excited to solve hard problems, work with a team of builders, and help hospitals deliver better care every day, we'd love to meet you! As a Data Science Lead, you will play a pivotal role in designing, developing, and deploying machine learning models that drive AI-powered automation across healthcare operations. You will own end-to-end ML lifecycle management, ensuring operational excellence, measurable business impact, and collaboration with cross-functional teams. Your work will enable hospitals and healthcare facilities to deliver better, faster, and more cost-effective care.If you’re passionate about building impactful ML solutions, leading data science teams, and transforming care delivery operations, join Kontakt.io http://Kontakt.io and help us redefine the future of healthcare! Responsibilites - Own the full lifecycle of assigned ML models — from ideation to deployment and post-launch validation. - Translate business goals into measurable data science objectives (e.g., improve workflow efficiency or reduce operational latency). - Design and execute robust A/B or interleaved tests to quantify model impact; define success metrics before deployment. - Deliver production-ready, well-documented code following internal engineering standards (testing, CI/CD, peer review). - Package and deploy models as services (APIs, microservices), treating deployment as an integral part of development. - Maintain operational reliability, scalability, and performance of all owned models and pipelines. - Build dashboards and alerts for model health, drift detection, and SLA compliance. - Continuously monitor for degradation, bias, or data drift; proactively resolve issues. - Participate in on-call rotation for ML systems; enable team to serve as primary responder for incidents related to owned models and data services. - Lead root cause analysis (RCA) within 48 hours of production incidents and document remediation actions. - Serve as the internal subject-matter expert for your domain (e.g., patient journey, asset utilization). - Partner with Product, Engineering, and Leadership to communicate insights, model limitations, and roadmap priorities. - Identify high-value data sources and upstream improvements to improve model outcomes or enable new capabilities meaningfully. - Ensure all initiatives are tied to clear metrics or business KPIs. What You Bring - Proven leadership experience with the ability to drive technical strategy while mentoring a high-performance Data Science and ML Engineering team. - 10+ years of experience in Data Science, Machine Learning, or related roles. - Strong proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, or Scikit-learn). - Experience with production ML systems, including model deployment, monitoring, and lifecycle management. - Familiarity with cloud platforms (AWS) and scalable ML infrastructure. - Strong understanding of data engineering, feature engineering, and model evaluation metrics. - Experience with real-time systems, RTLS, or healthcare data - Knowledge of healthcare regulations and EHR systems (Epic, Cerner, Meditech) Ready to Build the Future of Healthcare? Apply now and help to build the platform that care operations run on. 🚀