Data Scientist, Senior - WFH
About the Company
Junction is building an infrastructure platform for diagnostic healthcare, helping make patient data more accessible, actionable, and automated across laboratories, medical devices, and clinical workflows. The company aims to use health data to generate deeper insights into human health and disease, supporting more personalized, proactive, and affordable healthcare. Backed by Creandum, Point Nine, 20VC, Y Combinator, and other investors, Junction is scaling its platform and expanding how healthcare data supports clinical and business decisions.
About the Role
Junction is seeking its first Data Scientist to establish the company's data science methodology, research priorities, and analytical decision-making framework. This role will lead high-impact statistical modeling, experimentation, and evaluation work across diagnostics, clinical workflows, and AI-enabled product features. You will work closely with data, product engineering, and company leadership to determine what should be measured, which analytical methods are appropriate, and how findings should influence product strategy and business outcomes. The role combines strategic ownership with hands-on technical work, from developing models and validating results to helping integrate analytical systems into production. This is a fully remote opportunity for candidates physically located in eligible GMT or EST time zones. Applicants must be able to work in one of these time zones, and visa sponsorship is not currently available.
Key Responsibilities
Own research and modeling initiatives across diagnostics, clinical workflows, and AI-enabled product development. Establish rigorous frameworks for measurement, experimentation, and causal evaluation to distinguish meaningful signals from noise and support defensible decisions. Develop predictive models, segmentation methods, risk assessments, routing logic, and other statistical systems that inform product and business strategy. Build the analytical foundations for customer-facing features, including model development, validation, performance tracking, and ongoing evaluation. Collaborate with engineering and data engineering teams to move analytical work from prototypes into reliable production systems. Define evaluation methodologies, quality thresholds, monitoring practices, and performance reviews for data-driven and AI-enabled features. Set research priorities and identify opportunities where statistical modeling and experimentation can create meaningful business and customer value. Communicate technical findings, methodological limitations, trade-offs, and recommendations clearly to technical teams, executives, and other stakeholders. Establish consistent standards for how Junction measures the quality, reliability, and real-world effectiveness of its models and analytical systems. Take ownership of ambiguous problems, determine the most valuable questions to investigate, and deliver practical solutions.
Required Qualifications
Demonstrated experience leading high-impact analytical work that has influenced product, operational, or business decisions. Strong foundation in statistical inference, experimental design, observational analysis, and model evaluation. Proficiency in Python and/or R, with experience analyzing large, complex, real-world datasets. Experience developing predictive or decision-support models for production or near-production environments. Experience partnering with engineering teams to move analytical work from research or prototypes into deployed systems. Ability to operate strategically while remaining hands-on with data, modeling, evaluation, and technical problem-solving. Strong communication and stakeholder management skills, including the ability to explain analytical methods, findings, and trade-offs to both technical and non-technical audiences. Comfort working in a startup environment with ambiguity, limited structure, and significant individual ownership.
Preferred Qualifications
Experience designing, conducting, and publishing research studies. Familiarity with HIPAA, protected health information (PHI), or other clinical regulatory frameworks. Experience with modern data infrastructure and production workflows, including data warehouses, orchestration, and transformation tools. Experience developing and evaluating large language model (LLM) or AI-powered features in customer-facing products. Background working directly with healthcare, diagnostic, laboratory, wearable, or other clinical datasets. Practical experience applying causal inference techniques, such as difference-in-differences, propensity scoring, or instrumental variables. Salary & Benefits The compensation description lists a base salary of $180,000–$220,000 per year, plus equity. The listing header separately displays $180,000–$200,000, so candidates should confirm the applicable salary range with the recruiting team. Final compensation depends on location and experience. Additional benefits include Equity, with an extended exercise period after two years of employment. Company and team offsites. A monthly learning and productivity budget of $150. A monthly wellness budget of $100. Remote-first working arrangements and $1,000 for home office equipment. A coworking budget. 36 days of paid time off, including bank holidays. Premium healthcare coverage. A monthly employer contribution of 4% toward pension or 401(k). An annual blood test covered by the company. Enhanced parental, foster, and adoption leave. Role Expectations This position focuses on statistical research, modeling, experimentation, and translating analytical findings into measurable product and business outcomes. It is not primarily a dashboarding or reporting role, nor is it an ML infrastructure position. The successful candidate will take responsibility for identifying high-value analytical problems, selecting appropriate methods, and ensuring models and AI-enabled features perform reliably in real-world use.