Data Science

Description

Sentra is the global leader in cloud-native data security for the AI era. The company’s mission is to empower organizations to confidently scale their data operations across multi-cloud and on-premises environments while leveraging the power of AI without compromising security.

Sentra’s unique approach enables enterprises to autonomously scan their environments without the need for agents, ensuring that data remains securely within their cloud or on-premises infrastructure. This innovative methodology sets us apart in the industry, providing organizations with control and visibility over their sensitive data at all times. Our commitment to excellence in data security posture management and data detection and response makes Sentra a leader in the field.

As a Data Scientist, you will lead research and development of advanced AI models - from LLMs and NER systems to generative and self-supervised architectures - that power Sentra’s data classification capabilities. You’ll explore emerging AI techniques, build prototypes and bring them to production.

Responsibilities

Research & Innovation – Design and evaluate novel AI and NLP approaches (LLMs, generative models, self-supervised methods) to enhance entity recognition and contextual understanding.

Prototype to Production – Develop proof-of-concept models and collaborate with MLOps and engineering teams to harden and deploy them in production environments.

Experimentation Frameworks – Build tools and pipelines for large-scale experimentation, model evaluation, and continuous improvement.

Data-Driven Insights – Leverage Sentra’s unique datasets to define key metrics, drive iterative improvements, and balance precision-recall trade-offs.

Cross-Functional Collaboration – Work closely with Product and Engineering to align research direction with business and customer impact.

Requirements

7+ years of experience in machine learning or AI research, with deep expertise in NLP, LLMs, or Generative AI.

Proven track record of delivering AI models into production, including model deployment, monitoring, and lifecycle management.

Strong proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow).

Hands-on experience with large-scale data processing (Spark, Ray, or similar) and cloud platforms (AWS, GCP, or Azure).

Solid understanding of MLOps practices, including continuous training, evaluation, and model versioning.

Excellent communication and collaboration skills, especially when working across product and engineering teams.

Proffered Qualifications

Experience in data security, privacy, or compliance domains.

Familiarity with LLM fine-tuning, and prompt optimization.

Experience with cloud-based AI training and fine-tuning frameworks.

Advanced degree (Master’s or Ph.D.) in Computer Science, Machine Learning, or related field.