Why This Job is Featured on The SaaS Jobs
Product Operations roles matter in SaaS because they sit at the intersection of product, data, and execution—where taxonomy, data quality, and internal tooling directly influence how a platform scales. This L2 Product Operations position centers on building rule-based classification using NLP and structured analysis, a common need in SaaS products that manage large catalogs or complex datasets and rely on consistent metadata to power search, reporting, and downstream automation.
From a career standpoint, the work builds durable SaaS skills: translating ambiguous product data into operational rules, instrumenting quality checks across a lifecycle, and partnering with analytics and reporting teams to support launches. The emphasis on SQL, Excel depth, and basic Python reflects a pragmatic “ops meets analytics” toolkit that transfers well across SaaS environments where product teams need reliable data foundations rather than one-off analyses.
The role is best suited to early-career professionals who enjoy structured problem-solving and can balance precision with delivery ownership. It fits someone comfortable working across stakeholders, iterating on classification logic, and treating data hygiene as a product surface—particularly those looking to grow into product ops, analytics engineering, or data-focused product roles within SaaS.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
L2 Product Operations - Marketshare
Qualifications and Requirements:
The ideal candidate for this role should possess strong analytical and problem-solving skills, the ability to prioritize and manage multiple tasks in a fast-paced environment, and relevant work experience of 1-2 years as a Product Analyst or Research Analyst within an e-commerce organization. In addition, they should have:
- Strong SQL skills
- Basic knowledge of Python programming
- Proficient in MS Excel (including Vlookup, pivot analysis, and advanced formulas)
- Excellent written and verbal communication skills, able to clearly and effectively convey information
Key Responsibilities:
- Develop a rule system using NLP models to categorize large amounts of product data
- Analyze data patterns to classify products into the appropriate categories
- Maintain data quality throughout the product development life cycle
- Take ownership of the project from conceptualization to final delivery
- Collect, gather, and analyze data according to established business rules
- Collaborate with other analytics and reporting development teams to ensure a seamless product launch
- Work with stakeholders to improve the operational efficiency of the team.
