Staff Data Scientist

Proofpoint; Toronto; On-site; CAD 120,000 - 180,000; Full time

Job summary

An innovative company is looking for a Staff Data Scientist to spearhead cutting-edge machine learning and generative AI initiatives. In this pivotal role, you will lead the design and deployment of advanced AI solutions that address complex cybersecurity challenges. With a focus on collaboration, you'll work alongside cross-functional teams to ensure the successful implementation of scalable AI systems. This is an exciting opportunity for a seasoned professional to drive impactful change in a dynamic environment, where your expertise will shape the future of AI in cybersecurity. If you thrive in a fast-paced setting and are passionate about AI, this role is for you.

Qualifications

• 10+ years in data science or applied ML, with 3+ years in leadership.
• Experience deploying ML and GenAI solutions at scale.

Responsibilities

• Lead development of ML models and analytics solutions.
• Collaborate with engineering teams to build scalable AI systems.

Skills

Machine Learning

Generative AI

Data Science

Python

Leadership

Statistical Analysis

MLOps

Collaboration

Education

PhD or Master’s in Computer Science

Tools

PyTorch

TensorFlow

Docker

Kubernetes

AWS

GCP

Azure

LangChain

Hugging Face

Job description

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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

1. Lead the development of machine learning models and advanced analytics solutions to solve complex business problems.
2. Design and implement generative AI and large language model (LLM) applications, including fine-tuning and domain adaptation for cybersecurity use cases.
3. Collaborate with engineering teams to build scalable and secure LLM-based systems (retrieval-augmented generation, prompt engineering, evaluation pipelines).
4. Architect and lead AI solutions across full lifecycle—from experimentation to MLOps pipelines and production deployment.
5. Design experiments and use statistical analysis to measure the impact of various business strategies.
6. Oversee the deployment of machine learning and LLM models in production, ensuring performance, scalability, and responsible AI practices.
7. Lead the development of model performance monitoring, observability, and continuous learning pipelines.
8. Define technical direction for LLM and GenAI adoption, including benchmarking open-source and commercial models.
9. Champion AI/ML best practices including model governance, reproducibility, and ethical AI considerations.
10. Promote a data-driven and AI-forward culture within the organization and advocate for cutting-edge AI adoption across teams.
11. 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.

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