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Own Suki’s growth product strategy by defining activation, engagement, and retention metrics; analyzing funnels; building dashboards; instrumenting events; designing experiments; and translating insights into product requirements and roadmap priorities. Partner with Engineering, Data, Customer Success, Sales, and clinical stakeholders to improve clinician adoption and retention in a B2B SaaS healthcare environment.

The Senior Machine Learning Engineer will develop NLP solutions, manage datasets, optimize performance metrics, and work on ML applications for healthcare.

Build and deploy customer integrations using Suki Platform APIs and SDKs. Scope customer needs, design solutions combining dictation and generative AI capabilities, estimate timelines, deploy distributed applications across on-premises and cloud environments, and guide implementations through production. Translate technical decisions for clinical, executive, and engineering stakeholders while relaying customer feedback to internal engineering and data science teams.

Lead Suki’s end-to-end demand generation strategy across customer segments and revenue streams. Build acquisition and expansion programs, refine audiences and account targeting, develop new channels, and coordinate integrated campaigns across paid, digital, content, events, ABM, and executive engagement. Partner with Sales and senior commercial leaders to connect marketing activity to pipeline and revenue. Diagnose funnel performance, run experiments, optimize investments, and establish measurement across engagement, opportunities, expansion, and revenue.

Own net-new partner sales for the Suki Platform in EHR, telehealth, care management, and revenue cycle verticals. Build C-suite and clinical/IT relationships, qualify and close new logo opportunities, maintain pipeline and forecasts in Salesforce, collaborate with product/marketing/customer success, translate client needs into platform integrations, and travel extensively (40%–60%).