Jesta I.S.

Jesta I.S.

Senior Data Scientist

Montreal, Quebec · Senior

Sponsorship not specifiedDetected 30 days ago
PythonFastAPICode ReviewGitSQLSnowflakeAWSAzureDockerCI/CDDevOpsAPI DevelopmentRESTMachine LearningDeep LearningTensorFlowPyTorchscikit-learnPandasNumPyData AnalysisData EngineeringData ScienceComputer Vision

About the role

  • We are seeking a Senior Data Scientist with deep expertise across the full spectrum of modern AI - from classical predictive modelling to large language models, agentic AI systems, and computer vision.
  • The successful candidate will take full ownership of our existing production AI stack - maintaining, improving, and scaling models and pipelines already in active client use - while contributing meaningfully to the development of new capabilities.

Responsibilities

  • Perform exploratory data analysis (EDA), model diagnostics, and data quality assessments.
  • Design and execute model experiments, hypothesis testing, oracle testing, and statistical evaluations.
  • Own the full model lifecycle from development and backtesting to deployment, monitoring, drift detection, and retraining.
  • Design and implement LLM-powered applications using RAG, fine-tuning, prompt engineering, structured outputs, and vector databases.
  • Build AI systems that securely interact with enterprise data through governed APIs.
  • Develop explainability and transparency mechanisms for enterprise AI solutions.
  • Design and develop autonomous AI agents with multi-step reasoning and tool use.
  • Build integrations using Model Context Protocol (MCP) and tool-calling architectures for ERP data access.
  • Implement human-in-the-loop (HITL) workflows, role-based security, and approval mechanisms.
  • Design Semantic Read APIs connecting AI models to ERP data securely and reliably.

Requirements

  • Candidates with a Master's degree and exceptional industry experience will also be considered.
  • Experience with signal processing techniques is an asset.
  • Experience with ERP systems, retail data models, or supply chain data is an asset.
  • This is a senior individual contributor role requiring strong autonomous judgment, technical leadership, and the ability to define and execute complex AI workstreams with minimal supervision.
  • Minimum 5 years of industry experience in applied data science (excluding academic research, internships, and research positions).
  • Working knowledge of MCP or comparable agent orchestration frameworks.
  • Education
  • Experience
  • Proven experience deploying and maintaining production-scale ML systems.
  • Experience owning existing AI platforms as well as developing new capabilities.
  • Hands-on expertise with LLMs and Generative AI, including RAG, prompt engineering, structured outputs, and evaluation frameworks.
  • Experience building agentic AI systems with tool use, orchestration, and multi-step reasoning.
  • Experience developing and deploying computer vision models.
  • Demonstrated ability to independently lead complex technical initiatives.
  • Experience in commercial demand forecasting or time series modelling is a strong asset.

Nice to have

  • PhD in Artificial Intelligence, Data Science, Computer Science, or a related quantitative field strongly preferred.

Skills

  • R is an asset.
  • Forecasting libraries including Prophet, DeepAR, ARIMA/SARIMA, Croston/TSB, and ensemble methods.
  • Experience with Kedro or similar workflow orchestration frameworks and large-scale batch processing.
  • MLOps expertise including MLflow, Docker, CI/CD, Git, experiment tracking, model registries, drift detection, and retraining strategies.
  • Experience with AWS (SageMaker, Lambda, S3, IAM, Batch), Azure ML, Azure DevOps, and Snowflake/Snowpark.
  • REST API development and integration using frameworks such as FastAPI or Flask.
  • Advanced SQL for enterprise-scale analytics.
  • Soft Skills
  • Excellent written and verbal communication skills with technical and executive audiences.
  • Strong analytical rigor and sound technical judgment.
  • Collaborative mindset with cross-functional engineering and MLOps teams.

Benefits

  • Health coverage (medical, dental, disability, and life insurance)
  • Wellness program (gym membership reimbursement)
  • Flexible schedule
  • Design, develop, and optimize machine learning models for demand forecasting, inventory optimization, and pricing analytics.
  • Evaluate and benchmark forecasting approaches including LightGBM, Random Forest, Gradient Boosting, Deep Learning (PyTorch), DeepAR, ARIMA/SARIMA, Prophet, ensemble methods, and Croston/TSB.
  • Computer Vision
  • Integrate multimodal vision capabilities into forecasting and AI agent workflows.
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