> Markdown version of [/jobs/ext/2710600-data-scientist-lead](https://www.wearedevelopers.com/jobs/ext/2710600-data-scientist-lead). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist Lead - **Company:** KONTAKT LLC - **Location:** New York, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Cerner, Application Programming Interfaces (APIs), Amazon Web Services, Cloud Computing, Continuous Integration, Data as a Services, Information Engineering, Python (Programming Language), Machine Learning, Tensorflow, Real Time Systems, Feature Engineering, EHR Systems, Pytorch, Model Validation, Scikit Learn, Machine Learning Operations, Meditech, Microservices - **Published:** September 4, 2026 - **Apply:** https://startup.jobs/data-scientist-lead-kontakt-io-8323566 ## About the Role * 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? ## Description * 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. ## Related Videos - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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