> Markdown version of [/jobs/ext/3044236-applied-data-scientist](https://www.wearedevelopers.com/jobs/ext/3044236-applied-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Applied Data Scientist

- **Company:** Gitkraken
- **Location:** Alicante (Alacant), Spain
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Command-Line Interface, Cloud Computing, Data Warehousing, Cursor, Programming Tools, Monitoring of Systems, Python (Programming Language), Machine Learning, Operational Data Store, TypeScript, Workflow Management Systems, Datadog, Large Language Models, Snowflake, Backend, Enterprise Integration
- **Published:** September 24, 2026
- **Apply:** https://www.adzuna.es/contact-us.html

## About the Role

Deep experience in machine learning, applied AI, or a similarly hands-on product data role at a Senior level
A track record of shipping data or ML-powered capabilities into real products or operational workflows
Comfort moving from messy problem statements to practical execution without a lot of structure
Ability to work across the stack, not just in notebooks
Strong product judgment and a bias toward simple solutions that deliver measurable value
Experience deciding whether a problem is best solved with ML, rules, analytics, automation, or workflow design
Ability to balance speed and rigor, including knowing when "good enough to learn" is the right answer
Strong communication skills and the ability to explain tradeoffs clearly to technical and non-technical partners
Ownership mindset: you don't wait for perfect specs, and you follow through from idea to impact

## Description

At GitKraken, our goal is to help developers and their teams focus, create, and collaborate while minimizing distractions, context switching, and wasted time. Our developer experience platform supports millions of developers across desktop, command line, IDE, browser, web, and mobile.
We're looking for a pragmatic, startup-minded Senior Machine Learning Engineer or Applied Data Scientist who can take an idea from concept to production. Sometimes that idea will come from the data. Sometimes it will come from the business. In both cases, you'll be expected to determine what's possible, identify the fastest credible path forward, and ship solutions that create measurable impact.
This is a high-ownership role for someone comfortable working across data, product, and engineering. You should be able to frame ambiguous problems, explore messy data, build models or heuristics, integrate with production systems, measure outcomes, and iterate quickly. We care about practical impact, traction, and speed of learning. We are not looking for someone who waits for perfect specs or over-polishes a solution before proving it matters.
What You'll Do
Identify high-value opportunities from product, customer, and operational data
Evaluate ambiguous ideas quickly and determine what is feasible, useful, and worth shipping
Identify high-value opportunities from product, customer, and operational data
Build practical 80/20 solutions that create leverage quickly, then refine them based on traction
Own end-to-end execution across data exploration, modeling, experimentation, backend integration, and productization
Partner with engineering, product, design, and leadership to turn rough ideas into shipped capabilities
Use ML, analytics, heuristics, and automation pragmatically rather than forcing a model where one is not needed
Define success metrics, instrument outcomes, and improve solutions based on real-world usage
Help shape how GitKraken uses AI and data to improve developer workflows, team velocity, and product experience
Our Tech Lens
We value strong fundamentals over a rigid checklist and are always open to adopting new technologies, here is a snapshot of our current ecosystem:
Languages: Python (for data/ML execution), alongside Go and TypeScript across our core product and backend environments.
Data & Infrastructure: Snowflake for data warehousing, AWS for cloud infrastructure, and Datadog for monitoring and observability.
AI Ecosystem & DevEx: We live and breathe developer experience. We heavily leverage and build around modern AI development tools and LLMs like Cursor, Claude Code, and Codex to accelerate execution and shape the future of workflows.