Senior Product Data Scientist at Kake

Role overview

We're looking for a Product Data Scientist who is deeply focused on understanding user behavior and driving product decision-making through experimentation. This person will work closely with product managers, engineers, and design to identify opportunities, run structured A/B tests, and uncover insights that directly influence the product roadmap. The ideal candidate is comfortable owning experiments end-to-end — from hypothesis to rollout — and translating complex data into clear direction for the product team.

Responsibilities

• Design, build or validate software and data systems for product data science and analytics.
• Collaborate with product and engineering stakeholders on reliable delivery.
• Improve performance, testing, automation, documentation and observability.
• Translate requirements into maintainable production solutions.

Qualifications

• Solid skills in SQL and Python for data manipulation and analysis.
• Background in product analytics or product-facing data science roles.
• Ability to communicate insights clearly and influence product strategy using data.
• Excellent written and verbal communication skills in English.
• Ability to work independently in a remote, fast-paced environment.
• Latin America Timezone Overlap: 7h of daily overlap with GMT-3 required.

Technology in this role

• Python: Solid skills in SQL and Python for data manipulation and .
• SQL: Solid skills in SQL and Python for data manipulation and .
• Airflow: • Experience working with data warehousing and workflow tools (Snowflake, Airflow, Superset, Eppo, .
• Snowflake: • Experience working with data warehousing and workflow tools (Snowflake, Airflow, Superset, Eppo, .

Role-specific candidate guide

Preparation notes based on the employer’s stated responsibilities and qualifications.

Understanding the scope. This Senior Product Data Scientist position at Kake is best assessed through the work described in the employer’s posting, rather than the title alone. The available role overview says: “We're looking for a Product Data Scientist who is deeply focused on understanding user behavior and driving product decision-making through experimentation. This person will work closely with product managers,…” Read that statement together with the responsibilities below to understand the likely balance between delivery, coordination and technical decision-making. The role belongs to the AI and data area, but that label is only a guide to the work; the employer’s own requirements remain decisive. Before applying, check the original listing for reporting lines, product or team assignment, employment terms and any details that may have changed since this copy was checked. Where this page says a fact is not specified, treat it as an open question rather than an implied promise.

What the responsibilities signal. The posting specifically names “Design, build or validate software and data systems for product data science and analytics.”; “Collaborate with product and engineering stakeholders on reliable delivery.”; “Improve performance, testing, automation, documentation and observability.”. For each duty, identify a project where you performed comparable work and be ready to describe the context, your own contribution, constraints, decisions, and measurable result. Explain how you moved from the initial need to implementation, review, release or operational follow-through, where those stages apply. If the work involved other teams or users, distinguish your decisions from the group’s contribution. Avoid presenting a technology keyword as proof by itself: connect it to the problem you solved, the scale or complexity involved, and what changed after your work. The source may contain additional duties; review it directly before preparing your application.

How to read the requirements. The listed qualifications include “Solid skills in SQL and Python for data manipulation and analysis.”; “Background in product analytics or product-facing data science roles.”; “Ability to communicate insights clearly and influence product strategy using data.”. Mark each requirement as demonstrated, adjacent experience or a genuine gap, then use a concise example for the first two categories. A strong example explains what you personally owned, how you made trade-offs, how you validated the result and what you learned. If the posting specifies years of experience, language ability, certifications, location or eligibility, address those facts plainly in your CV or application rather than relying on the employer to infer them. Do not claim a qualification from exposure alone. For a requirement you do not meet, describe relevant transferable experience accurately and ask the recruiter whether it is mandatory.

Technology and working evidence. The technology terms associated with this listing are Python, SQL. Treat this as a set of posting-derived signals, not a guarantee that every tool is used daily or that the list is complete. For each term relevant to your experience, prepare one concrete example: the system or workflow, the part you owned, the alternatives considered, and how you tested.

Benefits

• Worldwide work arrangement as stated in the official posting.
• Opportunity to work on software, data and quality engineering projects.

About Kake

Kake describes itself as a remote-first community of experienced software engineers that helps client teams add individual engineers or full engineering groups. Its model combines technical vetting and human selection with AI-supported processes; Kake engineers work as dedicated members of client teams rather than as a separate outsourced delivery queue.

The company recruits across frontend, backend, mobile, quality engineering, data and DevOps. Its public hiring pages emphasize end-to-end ownership, collaboration with product and design, and engineering judgment. The exact client, product, team structure and employment terms can vary by opening, so candidates should verify those specifics in the individual listing.

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