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Proofpoint

Staff Data Scientist

Reposted 16 Days Ago
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In-Office
3 Locations
Expert/Leader
In-Office
3 Locations
Expert/Leader
Lead the development and deployment of machine learning models and generative AI solutions, collaborating cross-functionally and driving AI initiatives in cybersecurity.
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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

#LI-Remote

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

Top Skills

AWS
Azure
Docker
Faiss
GCP
Kubernetes
Pinecone
Python
PyTorch
Scikit-Learn
TensorFlow
Transformers

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