5 months ago
Remote, CanadaStaff+
Responsibilities
- Analyze data using Python, pandas, PySpark, and related tools to identify behavior patterns, content clusters, expertise dimensions, and other latent factors.
- Create, tune, and productionize models for categorization, classification, recommendations, similarity, prediction, and ranking.
- Design and implement AI-driven ingestion flows that convert unstructured tickets, emails, forms, messages, and logs into structured data.
- Build AI workflows that suggest metadata, request missing information, surface similar solutions, and safely automate simple Level 1 requests.
- Partner with engineers to integrate models and workflows into production systems with monitoring, fallbacks, and guardrails.
- Help product teams identify and scope AI opportunities and define reusable patterns for data ingestion, feature creation, model usage, and evaluation.
- Provide technical leadership, design and architecture guidance, and hands-on support for complex modeling and workflow automation challenges.
- Mentor junior data and ML engineers and analysts through reviews, pairing, and guidance on metrics, evaluation, and operational reliability.
- Establish and promote standards for experimentation, documentation, and responsible AI usage across teams.
Requirements
- 5+ years of experience in data science, ML engineering, or a similar applied role with a record of shipping production data or ML features.
- Strong Python and pandas skills, plus experience with PySpark or another distributed data processing framework.
- Understanding of supervised learning, classification, matrix factorization, embeddings, latent factor models, feature engineering, model evaluation, offline metrics, and online experiments.
- Proficiency with PyTorch or a similar deep learning framework and related ML tooling.
- Strong SQL skills and experience with modern data warehouses or data lakes.
- Experience integrating ML models into production systems through APIs and microservices, including performance and reliability considerations.
- Experience as a technical lead or senior individual contributor across multiple teams or projects.
- Experience translating business problems into data or ML projects and communicating tradeoffs to non-ML stakeholders.
- Track record of mentoring junior engineers or analysts and improving review, testing, monitoring, experimentation, documentation, and responsible AI practices.
- Preferred experience with LLMs, RAG, prompt engineering, fine-tuning, tool or agent orchestration, agent-assist features, automated operational workflows, MLOps tools, production model monitoring, or platform and enablement roles.
Categories
Data ScienceML Engineering
About Kaseya
Kaseya builds IT management and cybersecurity software for managed service providers and internal IT teams, delivered as a unified, subscription-based platform covering RMM, PSA, backup, endpoint protection, and SOC services. Founded in 2000 and headquartered in Miami, it is privately held and backed by Insight Partners. The company expanded its portfolio with acquisitions including Datto, and is used by MSPs and SMBs to automate and secure infrastructure.