Staff Data Scientist

Proofpoint; United States; Remote; USD 150,000 - 200,000; Full time

Job summary

Proofpoint is seeking a Staff Data Scientist / ML Engineer to spearhead various AI initiatives in cybersecurity. The role entails developing and deploying innovative machine learning solutions, leading projects with cross-functional teams, and continually enhancing AI capabilities through a data-driven approach.

Qualifications

• 10+ years in data science or machine learning; 3+ years in technical leadership.
• Hands-on experience with LLMs like OpenAI and frameworks like LangChain.
• Strong grasp of MLOps principles including CI/CD for ML.

Responsibilities

• Lead development of machine learning models to solve complex business problems.
• Design and implement applications using generative AI for cybersecurity use cases.
• Oversee deployment and monitoring of machine learning models in production.

Skills

Python

Machine Learning

Data Science

AI Solutions

LLM Fine-tuning

Education

PhD or Master’s degree in Computer Science, Data Science, Machine Learning, Statistics

Tools

PyTorch

TensorFlow

Scikit-learn

Docker

Kubernetes

Job description

It's fun to work in a company where people truly BELIEVE in what they're doing!

We're committed to bringing passion and customer focus to the business.

Proofpoint is hiring a Staff Data Scientist / ML Engineer to lead multiple Data Science, GenAI, and AI Engineering initiatives. The ideal candidate will drive the development and deployment of innovative machine learning and generative AI solutions, working cross-functionally with DevOps, product, and data engineering teams. This leadership role requires deep technical expertise, hands-on implementation experience, and a strong vision for scalable AI systems that power real-world applications in cybersecurity.

Responsibilities:

• Lead the development of machine learning models and advanced analytics solutions to solve complex business problems.

• Design and implement generative AI and large language model (LLM) applications, including fine-tuning and domain adaptation for cybersecurity use cases.

• Collaborate with engineering teams to build scalable and secure LLM-based systems (retrieval-augmented generation, prompt engineering, evaluation pipelines).

• Architect and lead AI solutions across full lifecycle—from experimentation to MLOps pipelines and production deployment.

• Design experiments and use statistical analysis to measure the impact of various business strategies.

• Oversee the deployment of machine learning and LLM models in production, ensuring performance, scalability, and responsible AI practices.

• Lead the development of model performance monitoring, observability, and continuous learning pipelines.

• Define technical direction for LLM and GenAI adoption, including benchmarking open-source and commercial models.

• Champion AI/ML best practices including model governance, reproducibility, and ethical AI considerations.

• Promote a data-driven and AI-forward culture within the organization and advocate for cutting-edge AI adoption across teams.

• Stay current with advancements in LLMs, GenAI, AI engineering, and emerging AI regulations.

Qualifications:

Education:

• PhD or Master’s degree in Computer Science, Data Science, Machine Learning, Statistics, or related discipline.

Experience:

• 10+ years of experience in data science or applied machine learning, with 3+ years in a technical leadership or managerial role.

• Proven track record of designing, developing, and deploying ML and GenAI solutions at scale.

• Hands-on experience working with LLMs (e.g., OpenAI, Anthropic, LLaMA, Mistral) and GenAI frameworks (e.g., LangChain, LlamaIndex, Hugging Face).

• Experience in cybersecurity or enterprise-scale threat detection systems is a strong plus.

Technical Skills:

• Proficiency in Python and relevant ML/AI libraries (e.g., PyTorch, TensorFlow, Transformers, Scikit-learn).

• Strong grasp of LLM fine-tuning, prompt engineering, RAG pipelines, vector databases (e.g., FAISS, Pinecone), and inference optimization.

• Experience with cloud platforms (AWS, GCP, Azure) and containerization tools (Docker, Kubernetes).

• Solid understanding of MLOps principles including CI/CD for ML, feature stores, model versioning, and monitoring.

• Familiarity with privacy, security, and compliance considerations in deploying AI solutions.

Soft Skills:

• Excellent leadership and mentorship skills, with a collaborative approach to cross-functional problem solving.

• Ability to communicate complex technical ideas to both technical and non-technical stakeholders.

• Strong innovation mindset, strategic thinking, and a passion for applying AI to impactful real-world problems

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If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!