Senior Data Scientist – AI/ML, Databricks & LLM

Job Description

Senior Data Analytical Specialist/Scientist

Client: Government Services Integration Cluster, Ministry of Public and Business Service Delivery and Procurement, Government of Ontario

Location: Toronto, ON – Onsite, 5 days per week

Office: 222 Jarvis St.

Contract: August 17, 2026 – December 31, 2026

Extension: Up to one additional term

Security Clearance: No clearance required

Position Overview

The Government of Ontario is undertaking a large-scale Data & AI transformation initiative, with the Ontario Data Platform (ODP) serving as the enterprise ecosystem for data ingestion, storage, processing, analytics, visualization, governance, quality, and data marketplace capabilities.

We are seeking a Senior Data Analytical Specialist/Scientist to support the development and execution of data science and AI/ML initiatives across government business areas. The successful candidate will work with technical and functional stakeholders to develop analytical solutions, execute machine learning and inference workloads, evaluate model performance, and transform complex data into actionable insights.

Key Responsibilities

• Lead the development and delivery of functional and ministry-specific analytics to support evidence-based decision-making.
  
• Execute and monitor batch inference jobs using Databricks and other cloud/data environments.
  
• Troubleshoot inference issues, investigate model anomalies, document results, and escalate issues when required.
  
• Assist with DSPy pipelines, including loading and configuring custom models and configurations.
  
• Support evaluation runs using established/internal metrics and methodologies.
  
• Track, analyze, and summarize performance metrics from inference and experimentation activities.
  
• Prepare, clean, transform, and validate structured and unstructured datasets, including legal text datasets.
  
• Annotate model outputs to support error analysis, benchmarking, and model evaluation.
  
• Develop and maintain reproducible Python scripts, notebooks, and analytical workflows.
  
• Work with stakeholders to understand current analytics and reporting capabilities and identify future-state requirements and opportunities for improvement.
  
• Own or contribute to analytics initiatives from data preparation through reporting and dataset delivery.
  
• Develop statistical models and machine learning algorithms to model business scenarios and derive valid inferences.
  
• Design methods for capturing, structuring, transforming, and processing data for analytical and machine learning purposes.
  
• Build reliable data models that provide accurate, understandable, and unbiased information.
  
• Work closely with Data Architects, ETL Developers, functional experts, and business stakeholders to develop appropriate Business Intelligence and analytical solutions.
  
• Participate in solution documentation, development, testing, implementation, and end-user training.
  
• Communicate complex quantitative analysis clearly to both technical and non-technical audiences through effective visuals, summaries, and recommendations.
  
• Provide analytical interpretation, advice, and guidance to client groups and stakeholders on converting analytics into actionable and proactive insights.
  
• Facilitate decision-making, manage expectations, and provide consulting and relationship-management support.
  

Requirements

Mandatory Technical Skills

Candidates must have:

• Hands-on experience with Microsoft Azure data tools/cloud technologies.
  
• Strong experience with Azure Databricks.
  
• Experience with MLflow and machine learning lifecycle/model experimentation environments.
  
• Strong proficiency in Python.
  
• Experience with Python data science libraries such as pandas, scikit-learn, PyTorch, or equivalent.
  
• Exposure to LLM frameworks, including DSPy, LangChain, Hugging Face, or equivalent.
  
• Experience working with data preparation, transformation, analysis, experimentation, and/or machine learning workflows.
  
• Ability to manipulate and analyze complex, high-volume data from structured and unstructured sources.
  

Data Science & Analytics Skills

• Statistical analysis and modelling.
  
• Data mining and machine learning.
  
• Machine learning algorithms and model evaluation.
  
• Natural language processing and related analytical disciplines.
  
• Data extraction, transformation, and loading concepts.
  
• Complex query development and query languages.
  
• Data modelling and database concepts.
  
• Data management and database architecture.
  
• Business and financial analysis.
  
• Information visualization and analytical reporting.
  
• Mathematics and statistics.
  
• Strong investigative, logical, analytical, and problem-solving abilities.
  
• Understanding of emerging Business Intelligence, Data Science, AI, and ML trends.
  

LLM, Databricks & Experimentation

The successful candidate should be comfortable working within modern AI/ML environments and supporting:

• LLM inference workflows.
  
• DSPy pipelines and evaluations.
  
• Other LLM frameworks such as LangChain or Hugging Face.
  
• Databricks batch inference and data processing.
  
• MLflow for experiment/model tracking and ML lifecycle activities.
  
• Model performance evaluation and metric tracking.
  
• Dataset annotation and error analysis.
  
• Reproducible notebooks and scripts.
  
• Model experimentation and benchmarking.
  

Additional Tools & Technologies

Experience with any of the following is considered valuable:

• R
  
• PowerPivot
  
• MATLAB
  
• SPSS
  
• SAS
  
• Microsoft Excel
  
• Microsoft Access
  
• VBA
  
• Relational and multidimensional data stores
  
• Business Intelligence and visualization platforms
  

General Qualifications

• Background in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a closely related discipline.
  
• Strong analytical, problem-solving, decision-making, and investigative skills.
  
• Excellent verbal and written communication skills.
  
• Strong interpersonal, collaboration, and teamwork abilities.
  
• Excellent consulting and relationship-management skills.
  
• Demonstrated ability to elicit requirements and develop or advise on analytical options and solutions.
  
• Ability to communicate effectively with both technical and non-technical audiences.
  
• Strong organizational skills and attention to detail, particularly when tracking experiments, model performance, and analytical deliverables.
  
• Ability to work effectively with multidisciplinary teams, stakeholders, architects, developers, and business experts.
  

Evaluation Weighting

• Technical Skills
  
• LLM Frameworks
  
• Databricks / MLflow
  

Work Arrangement

This is a fully onsite position requiring the successful candidate to work from the Toronto office at 222 Jarvis St., 5 days per week.

Ideal Candidate Profile

The ideal candidate combines strong Python and data science expertise with practical experience in Azure, Databricks, MLflow, and modern LLM frameworks. The candidate should be comfortable working hands-on with datasets, inference pipelines, model evaluation, experimentation, and analytical solutions while also communicating findings effectively to government stakeholders and business users.

Mandatory technology stack: Azure + Databricks + MLflow + Python + pandas/scikit-learn/PyTorch + LLM framework exposure (DSPy, LangChain, Hugging Face, or equivalent).

Requirements
Mandatory Technical Skills Candidates must have: Hands-on experience with Microsoft Azure data tools/cloud technologies. Strong experience with Azure Databricks. Experience with MLflow and machine learning lifecycle/model experimentation environments. Strong proficiency in Python. Experience with Python data science libraries such as pandas, scikit-learn, PyTorch, or equivalent. Exposure to LLM frameworks, including DSPy, LangChain, Hugging Face, or equivalent. Experience working with data preparation, transformation, analysis, experimentation, and/or machine learning workflows. Ability to manipulate and analyze complex, high-volume data from structured and unstructured sources. Data Science & Analytics Skills Statistical analysis and modelling. Data mining and machine learning. Machine learning algorithms and model evaluation. Natural language processing and related analytical disciplines. Data extraction, transformation, and loading concepts. Complex query development and query languages. Data modelling and database concepts. Data management and database architecture. Business and financial analysis. Information visualization and analytical reporting. Mathematics and statistics. Strong investigative, logical, analytical, and problem-solving abilities. Understanding of emerging Business Intelligence, Data Science, AI, and ML trends. LLM, Databricks & Experimentation The successful candidate should be comfortable working within modern AI/ML environments and supporting: LLM inference workflows. DSPy pipelines and evaluations. Other LLM frameworks such as LangChain or Hugging Face. Databricks batch inference and data processing. MLflow for experiment/model tracking and ML lifecycle activities. Model performance evaluation and metric tracking. Dataset annotation and error analysis. Reproducible notebooks and scripts. Model experimentation and benchmarking. Additional Tools & Technologies Experience with any of the following is considered valuable: R PowerPivot MATLAB SPSS SAS Microsoft Excel Microsoft Access VBA Relational and multidimensional data stores Business Intelligence and visualization platforms General Qualifications Background in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a closely related discipline. Strong analytical, problem-solving, decision-making, and investigative skills. Excellent verbal and written communication skills. Strong interpersonal, collaboration, and teamwork abilities. Excellent consulting and relationship-management skills. Demonstrated ability to elicit requirements and develop or advise on analytical options and solutions. Ability to communicate effectively with both technical and non-technical audiences. Strong organizational skills and attention to detail, particularly when tracking experiments, model performance, and analytical deliverables. Ability to work effectively with multidisciplinary teams, stakeholders, architects, developers, and business experts. Evaluation Weighting Technical Skills – 50% LLM Frameworks – 25% Databricks / MLflow – 25% Work Arrangement This is a fully onsite position requiring the successful candidate to work from the Toronto office at 222 Jarvis St., 5 days per week. Ideal Candidate Profile The ideal candidate combines strong Python and data science expertise with practical experience in Azure, Databricks, MLflow, and modern LLM frameworks. The candidate should be comfortable working hands-on with datasets, inference pipelines, model evaluation, experimentation, and analytical solutions while also communicating findings effectively to government stakeholders and business users. Mandatory technology stack: Azure + Databricks + MLflow + Python + pandas/scikit-learn/PyTorch + LLM framework exposure (DSPy, LangChain, Hugging Face, or equivalent).