Staff Applied Data Scientist

Cohesity is a leader in AI-powered data security and management. Aided by an extensive ecosystem of partners, Cohesity makes it easy to secure, protect, manage, and get value from data — across the data center, edge, and cloud. Cohesity helps organizations defend against cybersecurity threats with comprehensive data security and management capabilities, including immutable backup snapshots, AI-based threat detection, monitoring for malicious behavior, and rapid recovery at scale.

We’ve been named a Leader by multiple analyst firms and have been globally recognized for Innovation, Product Strength, and Simplicity in Design.

Join us on our mission to shape the future of our industry.

Want to help us simplify the world of data management?

Cohesity is looking for a Staff Data Scientist to spearhead advanced modeling, generative AI, and agentic workflows. This role combines deep expertise in statistical and machine learning methodologies ranging from causal inference and time-series forecasting to reinforcement learning with hands-on development of transformer-based LLMs, semantic search, and scalable AI system architectures. You’ll architect production-ready platforms, validate models through rigorous experimentation, and mentor a team to execute Cohesity’s AI vision.

Staff Data Scientist will lead Cohesity’s efforts in NLP, generative AI, agentic development, and advanced data science modeling driving design, validation, and deployment of complex algorithms and scalable AI platforms. You will translate business objectives into robust statistical and ML solutions, overseeing end-to-end experimentation, model governance, and performance optimization. 

HOW YOU'LL SPEND YOUR TIME HERE:

Advanced Modeling & Algorithm Development 

• Lead development of predictive and prescriptive models—including deep learning, reinforcement learning, time-series forecasting, and causal inference pipelines—ensuring scientific rigor and production readiness. 
• Design and execute statistical experiments and A/B tests, establishing robust validation frameworks and ensuring reproducibility. 

Experimentation & Performance Optimization 

• Design and implement robust experimentation frameworks—including A/B testing and causal inference methods—to evaluate AI-driven features and optimize product decisions. 
• Oversee performance profiling, benchmarking, and resource-efficient deployment strategies to meet strict latency and cost targets. 

Generative AI & NLP 

• Develop and fine-tune transformer-based LLMs (e.g., using LoRA, QLoRA, and full fine-tuning techniques) to optimize performance for domain-specific tasks.
• Implement Reinforcement Learning with Feedback (RLF) or Human Feedback (RLHF) to improve response quality and alignment with user intent.
• Optimize training and inference workloads using GPU acceleration (CUDA) and explore model deployment using NVIDIA Inference Microservices (NIM) for scalable, low-latency serving.
• Orchestrate multi-agent workflows and tool use with frameworks such as LangChain and OpenAI's Operator.
• Integrate retrieval-augmented generation (RAG) and semantic search capabilities using vector databases like Pinecone, Weaviate, or Milvus.
• Drive end-to-end delivery of customer-facing AI solutions by leveraging enterprise data catalogs to build domain-specific LLMs, enabling context-aware responses grounded in structured and unstructured data.

System Architecture & Performance 

• Architect scalable, distributed AI platforms leveraging cloud/serverless technologies (AWS Lambda, Azure Functions) and GraphQL APIs for real-time inference. 
• Drive performance profiling, benchmarking, and resource-efficient deployment strategies to meet strict latency and cost targets. 

Cross-Functional Collaboration 

• Partner with Product, Marketing, Finance, and Engineering teams to build scalable data pipelines and real-time dashboards that deliver actionable insights. 
• Translate complex modeling and AI concepts into clear narratives and presentations for stakeholders across the organization. 

Mentorship & Leadership 

• Mentor and coach data scientists and engineers through design reviews, pair programming, and career development. 
• Establish best practices in model governance, data privacy, and ethical AI, and drive their adoption across teams. 

WE'D LOVE TO TALK TO YOU IF