Publié il y a 3 mois
RQ11420 - Data Analytical Specialist/Scientist - Senior
@ Flexstaf IT
Toronto, Ontario
En bref
Flexstaf IT is hiring a senior data analytical specialist/scientist to support the Government of Ontario's Data & AI transformation and the Ontario Data Platform. The work covers running and monitoring batch inference jobs, supporting DSPy pipelines and evaluation runs, and preparing legal text datasets. The posting asks for Azure data tools, Databricks, MLflow and Python experience; no schedule, pay or work arrangement is stated.
Responsabilités :
- Execute and monitor batch inference jobs on Databricks
- Support DSPy pipelines and evaluation runs
- Prepare and clean legal text datasets
- Track and summarize inference performance metrics
- Maintain reproducible scripts and notebooks
Résumé des exigences :
- Azure data tools, Databricks, MLflow and Python experience
- Exposure to LLM frameworks (DSPy, LangChain, Hugging Face)
- Familiarity with pandas, scikit-learn or PyTorch
- Background in computer science, data science or AI/ML
- Strong organizational skills for tracking experiments
Outils et compétences
Source et dates
Type de source non vérifié · manatal
- Date publiée par l’employeur
- 2026-08-26
- Première détection par notre source
- 2026-10-11
- Mise à jour du dossier source
- 2026-10-11
Ces dates décrivent le dossier source, pas une vérification en direct du recrutement. Consultez l’offre originale.
Détails du poste
| Niveau | Senior |
| Domaine | Données et analytique |
| Milieu de travail | Bureau |
| Position | Surtout assis |
| Exigences physiques | physique faible · ordinateur élevé |
| Langues | English |
| Formation | Baccalauréat (préféré) |
| Domaines d’études | Computer Science, Data Science, AI/ML |
Résumé et détails rédigés par l’IA à partir de l’offre; l’offre de l’employeur fait foi.
Flexstaf IT
| Champ | Valeur |
|---|---|
| Offres sur Jobsy | 86 |
Must have:
must have experience Azure data tools - azure, databricks, MLflow, python
Exposure to LLM frameworks (DSPy, LangChain, Hugging Face, or equivalent).
Familiarity with Python and libraries like pandas, scikit-learn, or PyTorch.
Experience and Skill Set Requirements
Role Overview:
The Government of Ontario is embarking on a large Data & AI transformation initiative. Core to the transformation is the enablement of the Ontario Data Platform (ODP), which is being developed as the enterprise technology ecosystem for everything Data & AI. ODP is a modern ecosystem of data solution consisting of data ingestion technologies, storage, processing and visualization as well as solutions related to data governance, quality, marketplace, etc. ODP will initially serve all internal government clusters, ministries and program areas, but also external agencies in the future. We are looking for a data scientist to support various initiatives related to the platform as well as the execution of use cases to support the needs of various businesses within the government.
Key Responsibilities
· Run and Monitor Inferences
o Execute batch inference jobs on Databricks and other environments.
o Troubleshoot inference issues, log results, and escalate model anomalies.
· Pipeline & Evaluation Support
o Assist with running DSPy pipelines and loading custom models/configs.
o Support evaluation runs using internal metrics
o Track and summarize performance metrics for inference experiments.
· Data Handling & Experimentation
o Help prepare and clean legal text datasets for inference and evaluation.
o Annotate outputs to support error analysis and benchmarking.
o Maintain reproducible scripts/notebooks for inference runs.
Qualifications
· Background in Computer Science, Data Science, or AI/ML (student or recent grad is good).
· Familiarity with Python and libraries like pandas, scikit-learn, or PyTorch.
· Exposure to LLM frameworks (DSPy, LangChain, Hugging Face, or equivalent).
· Comfortable working with Databricks / MLflow or other cloud ML environments.
· Detail-oriented with strong organizational skills for tracking experiments.
Evaluation:
Technical Skills - 50%
LLM frameworks - 25%
Databricks/MLflow - 25%
Journal
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