The Role
Own end-to-end data science projects from scoping through production deployment. Design and evaluate models, build ML and AI workflows involving entity resolution, record linkage, embeddings, semantic similarity, and LLMs, and apply DataOps and MLOps practices. Develop reusable tools, collaborate with product and engineering, create technical documentation, review code, and mentor junior data scientists.
Summary Generated by Built In
Samba connects the advertising industry. Today, a trillion dollars of media flows through platforms that don’t talk to each other, and each one measures its own results. We built the independent alternative: consented first-party data and AI that give the world’s biggest advertisers, publishers, and platforms one clear view across all of it.
The next era of advertising will run on AI agents, and those agents will need data they can trust. We’re building that foundation, and the agents that act on it. Join us and help build what the industry runs on.
You own end-to-end delivery of significant data science projects with minimal guidance. You are a reliable, autonomous contributor with deep expertise in at least one of Samba’s core domains, identity and audience modeling, and the technical range to build production-ready solutions using modern machine learning (ML) and AI methods. You work closely with peers, product, and engineering.
- Own Delivery: Own end-to-end delivery of significant data science projects, from problem scoping and approach design through to production deployment.
- Decide and Document: Make sound, independent decisions on methodology, model selection, and evaluation, and document them in technical solution documents covering problem statement, approach, metrics, and timeline.
- Lead Solution Design: Lead solution design for your own initiatives, breaking epics into well-scoped user stories with clear acceptance criteria and applying DataOps and MLOps practices such as experiment tracking and model monitoring.
- Build ML Workflows: Implement advanced ML and AI-powered workflows, including entity resolution, probabilistic record linkage, embedding-based matching, semantic similarity, and large language model (LLM)-augmented pipelines.
- Build Shared Tools: Develop and maintain reusable tools, libraries, and documentation that raise team efficiency and technical standards, and give constructive, specific code review feedback.
- Partner Across Teams: Collaborate with product, engineering, and operations, translating business requirements into technical specifications, partnering with data engineering on scalable pipeline design, and joining cross-functional design reviews.
What You Bring
- Bachelor’s degree in Statistics, Data Science, Computer Science, Mathematics, or a related quantitative field, or equivalent practical experience; Master’s strongly preferred.
- 2+ years of hands-on data science experience, with a demonstrated ability to own and deliver complex, multi-sprint projects independently.
- Advanced Python (or another programming language) with production-quality code, testing, and documentation; strong SQL and PySpark for billion-row datasets.
- Databricks workflows, Delta Lake, and job orchestration (nice to have), plus working knowledge of Amazon Web Services (AWS) or Google Cloud Platform (GCP).
- Solid command of core ML (regression, classification, clustering, model evaluation, experimental design) on complex, high-volume data, with MLOps practices including experiment tracking and reproducible model deployment.
- Exposure to modern AI methods: retrieval-augmented generation (RAG) systems, LLM-augmented models, vector databases, and semantic search.
- A strong communicator and team player who turns technical work into clear documentation, user stories, and cross-functional conversations, and who can mentor junior data scientists and contribute to team standards.
Samba is an equal opportunity employer.
Your personal information
We collect personal information directly from you when you apply. Where the law permits, we may also receive it from third parties, such as through background, employment, or reference checks. For details, see Samba’s Applicant Privacy Policy. For EU residents, Samba Inc. is the data controller.
Use of AI in hiring
We may use AI tools to help review applications, analyze resumes, or assess responses. These tools support our recruiting team. People make every hiring decision. To learn more about how we process your data, please reach out.
Skills Required
- Bachelor's degree in Statistics, Data Science, Computer Science, Mathematics, or a related quantitative field, or equivalent practical experience
- Master's degree in a related quantitative field
- 2+ years of hands-on data science experience
- Experience independently owning and delivering complex, multi-sprint data science projects
- Advanced Python or another programming language, with production-quality code, testing, and documentation
- Strong SQL and PySpark experience with billion-row datasets
- Working knowledge of Amazon Web Services or Google Cloud Platform
- Experience with regression, classification, clustering, model evaluation, and experimental design
- MLOps experience, including experiment tracking and reproducible model deployment
- Exposure to retrieval-augmented generation systems, LLM-augmented models, vector databases, and semantic search
- Strong communication, documentation, user-story writing, and cross-functional collaboration skills
- Ability to mentor junior data scientists and contribute to team standards
- Experience with Databricks workflows, Delta Lake, and job orchestration
Am I A Good Fit?
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The Company
What We Do
Samba is a media-intelligence company that combines consented first-party viewership data from streaming, linear TV, the web, and video games with AI to map content, audiences, and outcomes. Its platform gives advertisers, publishers, and media platforms insight into audience behavior and supports campaign planning, targeting, measurement, and optimization across channels. Samba’s data and analytics help clients understand audiences and make marketing decisions.
