# Senior Data Scientist

**Company:** [Dataiku](https://hotfix.jobs/companies/dataiku)
**Location:** Unspecified
**Role:** Data Science
**Experience:** 8+ years
**Skills:** Python, SQL, Machine Learning, LLMs, MLOps, Data Engineering, Data Visualization, Dash, Streamlit, JavaScript, APIs, Cloud Architecture, Hadoop, Kubernetes, Dataiku
**Posted:** 2026-09-09

> Senior data scientist who designs and delivers production AI solutions directly with customers, from architecture and modeling through deployment and adoption. Requires 8 years in advanced analytics and AI, strong Python and SQL experience, and customer-facing communication skills.

## Job Description

## Responsibilities
- Design and co-develop production-grade AI projects with customers across industries and use cases.
- Enable users to learn and adopt the Dataiku platform through training sessions, office hours, workshops, and consultative support.
- Embed with client data, business, and engineering teams to contribute to end-to-end solution architecture, from data integration and pipeline design through production deployment.
- Make architecture trade-offs such as native platform functions versus custom code and batch versus real-time processing.
- Provide strategic guidance to customers and account teams to drive adoption, value realization, and long-term success.
- Shape solutions and proposals, including technical scoping and effort estimation.
- Coordinate sprints, prioritize tasks, and estimate delivery effort.

## Requirements
- 8 years of experience in advanced analytics and AI, ideally in consulting or a customer-facing data science or AI solution engineering role.
- 5 years of hands-on experience with Python and SQL.
- 5 years of experience designing, building, and deploying production-grade AI and machine learning models.
- Experience owning technical outcomes directly with customers through consulting, professional services, or embedded delivery models.
- Strong curiosity and ability to learn new technologies, tools, and techniques.
- Strong empathy, collaboration, knowledge-sharing, and communication skills.
- Ability to explain complex concepts to technical and non-technical audiences.

## Nice-to-haves
- Experience using large language models, agents, and machine learning to build real-world applications.
- Data visualization and application development with Dash, Streamlit, R Shiny, or JavaScript.
- Experience consuming or building APIs.
- Data engineering and MLOps experience.
- Familiarity with enterprise data science platforms and tools.
- Understanding of cloud architectures, Hadoop, or Kubernetes.
- Experience teaching, mentoring, or public speaking.

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