Cartesian Systems
Field Data Collection Specialist
- Confirmed live in the last 24 hours
- $65k–$80k
- Mid level
- On-site · Cambridge, MA
- 1+ yrs exp
- Added today
About this role
About Cartesian
Cartesian is building spatial intelligence for indoor environments to drive operational efficiency. We’re tackling one of the biggest challenges in the $35T global retail industry: in-store inventory visibility. Our platform delivers accurate indoor positioning and actionable product location insights, helping retailers streamline operations, optimize workflows, and reduce inefficiencies. By fusing wireless signals and mobile computer vision, we provide a uniquely scalable, infrastructure-free solution already deployed by international fashion brands.
Founded by an MIT engineering professor and alumni behind award-winning, patented core technologies, Cartesian spun out in 2023. Originally backed by a U.S. National Science Foundation SBIR Award, we’ve bootstrapped to a live product now deployed in multiple countries and are scaling aggressively.
About the Role
We are looking for a Field Data Collection Specialist to join Cartesian at a pivotal moment in our growth. This is a hands-on, impact-driven role focused on data collection, field operations, and R&D product testing. You will spend significant time in retail environments evaluating model performance, collecting and validating data, identifying issues, and helping ensure our technology performs reliably in real-world conditions.
Success in this role requires curiosity, attention to detail, and a strong sense of ownership. You should enjoy investigating problems, working directly with customers and in retail environments, and collaborating closely with technical team members to design and execute on critical data collection processes.
Location: In-person in Kendall Square, Cambridge, with regular travel to retail stores throughout the Boston area and occasional regional travel.
Salary: $65,000 - $80,000 annually
What You’ll Do
- Plan and run data collection protocols in real-world environments (e.g., retail stores, warehouses, …)
- Use and provide feedback on new data collection tooling for R&D and engineering teams
- Operate and maintain RFID and other collection devices, and troubleshoot hardware and app issues in the field
- Travel to customer sites to evaluate system performance under real-world conditions
- Collect, validate, and analyze datasets used to improve product accuracy and reliability
- Perform basic scripting, troubleshooting, and debugging to support testing activities
- Investigate bugs, edge cases, and unexpected behavior, providing detailed feedback to R&D and engineering teams
- Improve and document testing and data collection processes to increase quality, consistency, and efficiency
- Test new features, mobile applications, and workflows before deployment
- Help represent customer and field perspectives within the organization
Qualifications
- 1+ years of experience in a technical, operational, support, research, testing, or related role
- Strong attention to detail and ability to execute rigorous, repeatable testing processes
- Experience running scripts (e.g., in Python) and working with technical tools
- Strong problem-solving skills and ability to work independently in ambiguous environments
- Excellent written and verbal communication skills
- Professional, customer-facing demeanor
- Reliable, accountable, and highly organized
- Ability and willingness to travel regularly to retail locations
- Associate's or Bachelor's degree in a technical field or equivalent practical experience
Nice to Have
- Experience with product testing, field testing, QA, or beta testing
- Experience collecting, validating, or analyzing data
- Experience in retail, customer support, customer success, field operations, or deployment roles
- Familiarity with Python, SQL, or data analysis tools
- Experience supporting data pipelines, data quality monitoring, or operational tooling
- Spanish language proficiency
- Startup or early-stage company experience
Why Cartesian
- Work on hard, real-world problems with immediate and visible impact
- Join a small, highly technical team with significant ownership and autonomy
- Build systems that are deployed and used globally
- Collaborative, thoughtful, low-ego culture focused on learning and execution
- Opportunity to become a product expert and grow into roles across data operations, deployment, R&D, testing, and product
- In-person team culture in the heart of Kendall Square, Cambridge
If You’re Not Sure You’re a Perfect Fit
We know that no one checks every box—and we don’t expect you to. If this role sounds exciting to you, and you think you could contribute to Cartesian’s mission, we encourage you to apply even if your experience doesn’t perfectly match the description. We care deeply about people who are curious, thoughtful, and motivated to learn, and we believe strong teams are built from a diversity of backgrounds, experiences, and perspectives.
Interview Process
- Introductory conversation focused on motivation, background, and fit
- Technical exercise involving testing, analysis, and basic coding/problem-solving
- Follow-up technical discussion focused on reasoning, troubleshooting, and decision-making
- Final conversation with the hiring manager focused on ownership, communication, and culture fit
- References and offer
Written by Cartesian Systems. Original job post
Skills mentioned
- Data Analysis
- Data Pipelines
- Python
- SQL
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Cartesian Systems
Cartesian Systems builds an indoor spatial intelligence platform for retail that uses machine learning to enable positioning and perception capabilities. The company is hiring ML Scientists and engineers to develop and deploy perception models and features for enterprise retail customers.
- Industry
- Technology & Software
Likely interview questions
- Describe your experience with planning and executing data collection protocols in real-world environments, such as retail stores or warehouses.
- How would you approach troubleshooting hardware or app issues with RFID or other data collection devices in the field?