Sr Staff Data Scientist

Dexcom • San Diego, CA

2d • Remote

About The Position

The Company Dexcom Corporation (NASDAQ DXCM) is a pioneer and global leader in continuous glucose monitoring (CGM). Dexcom began as a small company with a big dream: To forever change how diabetes is managed. To unlock information and insights that drive better health outcomes. Here we are 25 years later, having pioneered an industry. And we're just getting started. We are broadening our vision beyond diabetes to empower people to take control of health. That means personalized, actionable insights aimed at solving important health challenges. To continue what we've started: Improving human health. We are driven by thousands of ambitious, passionate people worldwide who are willing to fight like warriors to earn the trust of our customers by listening, serving with integrity, thinking big, and being dependable. We've already changed millions of lives and we're ready to change millions more. Our future ambition is to become a leading consumer health technology company while continuing to develop solutions for serious health conditions. We'll get there by constantly reinventing unique biosensing-technology experiences. Though we've come a long way from our small company days, our dreams are bigger than ever. The opportunity to improve health on a global scale stands before us. Meet the team: As part of the Commercial Data Science & Revenue Operations team, you will join a team which is responsible for leveraging data and analytics to support sales/marketing revenue growth and drive data related innovation within the company. As Senior Staff Data Scientist, you will lead strategic commercial programs, with a strong focus on Sales Operations, AI-driven initiatives, sales effectiveness, and commercial performance measurement. This role will drive cross-functional projects that enhance operational efficiency, optimize sales processes, and deliver actionable insights through advanced analytics and reporting. The ideal candidate will partner with business leaders to drive sales related programs, implement scalable solutions, and ensure alignment with organizational goals. Where you come in: Lead complex, cross‑functional data science programs from discovery through production, aligning engineering, analytics, product, and compliance to deliver measurable business outcomes in medtech/biotech. Architect and evolve our cloud data stack (data lake/lakehouse, pipelines, CI/CD for analytics) with secure, compliant data operations and auditable lineage suitable for quality‑driven environments. Design, build, and productionize ML solutions (supervised classification/regression; unsupervised clustering/anomaly detection) using Python and Amazon SageMaker, with robust monitoring and model lifecycle management. Own AWS‑native development workflows leveraging Redshift, Lambda, and AWS CDK to create scalable, cost‑aware services that integrate cleanly with enterprise platforms. Establish alerting/notification systems and engineering runbooks that raise signal, increase reliability, and shorten mean time to detect/resolve issues across data/ML services. Define and instrument KPIs that quantify model performance, data quality, and business impact; automate alarms for drift, bias, and SLA breaches. Mentor and uplevel the team, setting technical direction, code and science standards, and coaching peers and junior scientists in experimental design and production engineering. Champion responsible AI and data governance, partnering with Privacy, Security, and Quality to ensure compliant use of data, robust documentation, and audit‑readiness.

Requirements

• Typically requires a Bachelor’s degree in a technical discipline, and a minimum of 13+ years related experience or a Master’s degree and 8+ years equivalent industry experience of a PhD and 5+ years of experience.

Nice To Haves

• Industry: Significant experience in medtech/biotech or similar regulated, quality‑driven settings; comfortable navigating audits, validations, and documentation expectations.
• Leadership: Proven track record leading software & data science projects and cross‑functional workstreams; strong mentorship history and ability to influence at senior levels.
• Cloud/ML: Active AWS Machine Learning Specialty certification; hands‑on with Python, Amazon Redshift, AWS CDK, AWS Lambda, and Amazon SageMaker.
• Architecture: Practical mastery of data lake design, batch/streaming data pipelining, CI/CD for analytics/ML, and secure, compliant data operations (access controls, logging, encryption, least‑privilege).
• Modeling: Demonstrated success delivering supervised and unsupervised algorithms into production with monitoring, A/B evaluation, and continuous improvement.
• Enablement: Experience building alerting/notification systems, reliability playbooks, and runbooks that improve efficiency and operational readiness for engineering teams.
• Communication: Ability to translate complex quantitative ideas for diverse audiences—executives, commercial partners, and engineering—through crisp narratives and visuals.
• Education: Preferred Master’s degree in a quantitative field (Data Science, Computer Science, Statistics, Engineering). Experience teaching graduate‑level courses and mentoring learners at an accredited university.

Responsibilities

• Lead complex, cross‑functional data science programs from discovery through production, aligning engineering, analytics, product, and compliance to deliver measurable business outcomes in medtech/biotech.
• Architect and evolve our cloud data stack (data lake/lakehouse, pipelines, CI/CD for analytics) with secure, compliant data operations and auditable lineage suitable for quality‑driven environments.
• Design, build, and productionize ML solutions (supervised classification/regression; unsupervised clustering/anomaly detection) using Python and Amazon SageMaker, with robust monitoring and model lifecycle management.
• Own AWS‑native development workflows leveraging Redshift, Lambda, and AWS CDK to create scalable, cost‑aware services that integrate cleanly with enterprise platforms.
• Establish alerting/notification systems and engineering runbooks that raise signal, increase reliability, and shorten mean time to detect/resolve issues across data/ML services.
• Define and instrument KPIs that quantify model performance, data quality, and business impact; automate alarms for drift, bias, and SLA breaches.
• Mentor and uplevel the team, setting technical direction, code and science standards, and coaching peers and junior scientists in experimental design and production engineering.
• Champion responsible AI and data governance, partnering with Privacy, Security, and Quality to ensure compliant use of data, robust documentation, and audit‑readiness.

Benefits

• A front row seat to life‑changing CGM technology.
• A full and comprehensive benefits program.
• Growth opportunities on a global scale.
• Access to career development through learning programs.
• An innovative, industry‑leading organization committed to our employees, customers, and communities.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees