> Markdown version of [/jobs/ext/4293447-data-scientist](https://www.wearedevelopers.com/jobs/ext/4293447-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).

---

# Data Scientist

- **Company:** Saransh Inc
- **Location:** Austin, TX, United States
- **Experience:** Experienced
- **Contract:** Temporary to permanent
- **Skills:** Artificial Intelligence, Amazon Web Services, Architectural Patterns, Microsoft Azure, Big Data, Cloud Computing, Continuous Integration, Data Architecture, Information Engineering, Distributed Systems, Python (Programming Language), Machine Learning, Data Streaming, Containerization, Kubernetes, Apache Kafka, Spark Streaming, Machine Learning Operations, Docker
- **Published:** October 26, 2026
- **Apply:** https://www.careerjet.com/jobad/us6a6157529b8c5ec8628692f90c16f9a6

## About the Role

8+ years of hands-on experience in Data Science and Data Engineering, with a proven track record of designing and deploying enterprise-scale analytical solutions.
Expert-level proficiency in Python, alongside deep knowledge of Architectural Patterns for distributed computing and large-scale data processing (a must-have requirement).
4+ years of dedicated experience in MLOps and Productionalization, focusing on the end-to-end lifecycle of machine learning models (CI/CD for ML).
Strong understanding of Data Architecture, and real-time streaming architectures (e.g., Kafka or Spark Streaming).
Experience with Cloud Infrastructure (AWS/Azure/GCP) and containerization (Docker/Kubernetes) to build scalable, resilient environment for model training and inference.
Excellent system design and analytical skills, with a focus on model governance, security, and scalability of AI solutions.
Superior communication and leadership skills, with the ability to align technical roadmaps with business strategy and explain architectural trade-offs to executive stakeholders.
Proven ability to lead cross-functional teams, define technical standards, and drive the adoption of innovative data technologies in a fast-paced environment.