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# Senior Data Scientist

- **Company:** KAKE,INC
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Airflow, Data Analysis, Data Infrastructure, Python (Programming Language), SQL Databases, Snowflake
- **Published:** August 24, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/n2zu197bg2

## About the Role

Strong foundation in Data Science with hands-on experience in experimentation, research, and product improvements.

Expertise in statistics, experiment design, and A/B testing, with a track record of delivering actionable insights.

Product-focused data science background - comfortable collaborating with PMs and communicating with both technical and non-technical stakeholders.

Proficiency in Python and SQL for data analysis, modeling, and reporting.

Excellent communication skills, with the ability to distill complex data into clear recommendations.

Proactive, ownership-driven mindset and willingness to go beyond assigned tasks.

Nice to Have

Experience with Snowflake, Superset, Eppo, or Airflow.

Background in SEO, acquisition, conversion optimization, or growth analytics.

Experience in consumer tech or high-growth product environments.

Previous remote work experience.

## Description

We're looking for a Senior Product Data Scientist with strong technical expertise and a true ownership mindset. This person will go beyond assigned tasks, proactively identifying opportunities for improvement and driving impact across the product. You'll work closely with Product Managers and cross-functional teams (design, engineering, marketing, research) to translate data into actionable insights that shape product direction.
ⓘ View potential timezone restrictions

The team operates around Latin America's time zone, so you'll need at least 6 hours of overlap with GMT-7.

What You'll Do

Partner with Product Managers and cross-functional stakeholders to inform product strategy with data-driven insights.

Design and run experiments (A/B testing, experimentation frameworks) to measure impact and guide decision-making.

Translate data into clear, actionable product recommendations.

Develop metrics, dashboards, and reports to monitor product health and growth.

Contribute to building scalable data infrastructure and experimentation workflows.


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