Senior Data Scientist Core AI R&D

Senior Data Scientist — Core AI R&D

Position: Senior Data Scientist — Core AI R&D

Experience: 5+ years (Senior); PhD in the area of time series is an asset

Employment Type: Full-time

About Us

We se advanced predictive maintenance analytics and interconnected data to surface actionable insights. These insights empower intelligent, data-driven decisions, driving continuous improvement and optimizing performance with our predictive maintenance solutions.

Position Overview

We are scaling our Core AI research team responsible for advancing the predictive and prescriptive intelligence at the heart of our platform. We are hiring a Senior Data Scientist with research-grade depth in one or more of: time-series modeling on industrial sensor data; reinforcement learning for prescriptive decision-making; and knowledge representation combined with multi-modal context.

You will join our established core R&D team and partner closely with applied product teams, the platform engineering team that operates our model training and serving infrastructure, and the team that integrates AI outputs into customer-facing workflows.

Key Responsibilities

Time-Series Modeling

• Develop and adapt modern deep-learning models for time-series on our proprietary industrial sensor data.
• Build retraining and adaptation pipelines so models stay accurate as live data streams evolve.
• Apply techniques that enable fast adaptation to new sensor sources and equipment types with limited labeled data.
• Drive measurable accuracy gains across our asset-class specific models.

  Reinforcement Learning & Prescriptive Decisions

• Design reinforcement-learning formulations that recommend the right operational action — what to do, when, and with what confidence.
• Build optimization approaches that balance multiple operational constraints.
• Apply preference-learning techniques using validated expert outputs as a learning signal.
• Partner with downstream teams to deliver prescriptive outputs into customer operational systems.

  Knowledge Representation & Multi-modal Context

• Extend our domain knowledge graph covering industrial assets, failure modes, and recommended actions.
• Apply graph-based learning to reason over asset relationships and to transfer knowledge across equipment types.
• Fuse multi-modal context — engineering diagrams, technical documents, operator notes, conversational data — into the predictive pipeline.
• Identify cross-asset patterns that improve generalization across customer segments.

  Research Execution & Collaboration

• Translate state-of-the-art research into production-grade implementations.
• Mentor junior data scientists and partner with product teams to land research outputs in customer-facing products.
• Contribute to publications, patents and open-source where appropriate.
• Define and instrument quality metrics for predictive and prescriptive model outputs.

Required Skills & Qualifications

Education

• PhD strongly preferred, or master's degree in Computer Science, Statistics, Electrical Engineering, Applied Mathematics, or a closely related discipline. Equivalent experience considered for exceptional candidates.

Technical Skills

• 5+ years of applied machine-learning experience with research-grade depth in at least one of: time-series modeling, reinforcement learning, or graph / knowledge-representation learning with multi-modal data.
• Hands-on experience building and adapting modern deep-learning models on real-world data streams.
• Strong Python; fluency in a major deep-learning framework (PyTorch preferred).
• Working familiarity with modern reinforcement-learning techniques and at least one production-grade RL framework.
• Experience with graph-based ML — knowledge graphs, embeddings, graph neural networks — or equivalent structured-data representation learning.
• Experience with retrieval-augmented and vector-database-backed approaches for working with unstructured context.
• Comfort with a major cloud provider and its managed ML services.
• Solid software engineering practices — version control, testing, reproducible experimentation.

Soft Skills

• Bias to ship — iterate quickly on early prototypes.
• Ability to translate research into shippable product capabilities, not just papers.
• Strong written and verbal communication — able to explain technical approaches to both peers and non-technical stakeholders.
• Comfort with ambiguity, prioritization, and operating across time zones.

  Good to Have

• Domain exposure to predictive maintenance, condition monitoring, vibration analysis, or industrial AI applications.
• Experience with physics-informed ML or causal inference for root-cause analysis.
• Publications at top ML venues or relevant industry conferences.
• Familiarity with modern agentic AI frameworks or LLM evaluation approaches.