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Data Optimisation Jobs in Ontario (NOW HIRING)

Data Scientist

Cambridge, ON · Hybrid

CA$64K - CA$114K/yr

Basic understanding of optimization concepts (linear, integer, dynamic programming) * Experience in ... Pull data from various systems using SQL, PySpark, and other standard tools for relational and ...

Data scientists on this team apply supervised and unsupervised machine learning, statistical modeling, causal inference, optimization, and experimentation to some of the most consequential risk ...

New

Design and provision certified, highly optimized, and well-documented datasets. This enables data-savvy power users across marketing and operations to safely build their own ad-hoc analyses without ...

The Applied RWD Scientist serves as a methodological authority and RWD data domain expert, ensuring best-in-class data selection and optimal data usage to generating reliable insights or evidence.

The Applied RWD Scientist serves as a methodological authority and RWD data domain expert, ensuring best-in-class data selection and optimal data usage to generating reliable insights or evidence.

Experience with SQL performance tuning and optimization. Experience in Agile development methodology. Knowledge of database design ,data modeling. Experience with oracle tools such as SQL Developer ...

25-167 Data Engineer

Oshawa, ON · Hybrid

$75 - $95/hr

Develop optimized, performant data pipelines and models at scale using technologies such as Python, Spark and SQL, consuming data sources in XML, CSV, JSON, REST APIs, or other formats. Document as ...

This role focuses on analytics, reporting, and process optimization , with exposure to automation and AI powered tools as part of everyday analytical work. Data Analysis & Insights * Analyze large ...

The Applied RWD Scientist serves as a methodological authority and RWD data domain expert, ensuring best-in-class data selection and optimal data usage to generating reliable insights or evidence.

25-199 - Data Engineer

Oshawa, ON · Remote

$85 - $95/hr

Develop optimized, performant data pipelines and models at scale using technologies such as Python, Spark and SQL, consuming data sources in XML, CSV, JSON, REST APIs, or other formats. Document as ...

The Applied RWD Scientist serves as a methodological authority and RWD data domain expert, ensuring best-in-class data selection and optimal data usage to generating reliable insights or evidence.

The Applied RWD Scientist serves as a methodological authority and RWD data domain expert, ensuring best-in-class data selection and optimal data usage to generating reliable insights or evidence.

Showing results 41-60

Data Optimisation information

What are the most common challenges faced in a data optimisation role, and how can I prepare for them?

One of the main challenges in a Data Optimisation role is dealing with large, complex datasets that may have inconsistencies or missing information. You’ll often need to balance improving data quality with maintaining data integrity and system performance. Collaborating across departments, such as IT, analytics, and business operations, is typical, so strong communication skills are essential. Preparing by learning best practices in data cleaning, ETL processes, and familiarizing yourself with relevant tools will help you succeed and adapt quickly.

What are the key skills and qualifications needed to thrive as a data optimisation specialist?

To thrive as a Data Optimisation Specialist, you need strong analytical skills, proficiency in data analysis, and a background in statistics or computer science, often supported by relevant degrees or certifications. Familiarity with data management tools like SQL, Python, Excel, and optimisation platforms such as Google Analytics or Tableau is typically required. Excellent problem-solving abilities, attention to detail, and effective communication are essential soft skills for translating insights into actionable strategies. These skills ensure that data-driven decisions are accurate, impactful, and aligned with business objectives.

What are data optimisation jobs?

Data optimisation jobs involve analyzing and improving data quality, structure, and efficiency to support better decision-making and operational performance. These roles often require skills in data analysis, database management, and tools like SQL or data visualization software, with a focus on enhancing data accuracy and accessibility.

What is data optimisation?

Data optimisation refers to the process of improving the quality, accessibility, and efficiency of data within an organization. It involves cleaning, structuring, and organizing data so that it can be used more effectively for analysis, decision-making, and business operations. Data optimisation can help reduce storage costs, enhance system performance, and ensure that accurate and relevant data is available when needed. This process often includes data deduplication, compression, and the implementation of best practices for data management.

What is the difference between Data Optimisation vs Data Analysis?

AspectData OptimisationData Analysis
Primary FocusImproving data processes and system efficiencyInterpreting data to uncover insights
Skills RequiredData management, process improvement, technical skillsStatistical analysis, reporting, critical thinking
Work EnvironmentIT teams, data engineering, system optimizationBusiness units, research teams, analytics departments
CertificationsData management, database certificationsData analysis, statistical certifications

Data Optimisation focuses on enhancing data systems and processes for efficiency, while Data Analysis involves examining data to generate insights. Both roles require strong technical skills, but their objectives differ: one improves data infrastructure, the other interprets data for decision-making.

What are popular job titles related to Data Optimisation jobs in Ontario? For Data Optimisation jobs in Ontario, the most frequently searched job titles are:
What job categories do people searching Data Optimisation jobs in Ontario look for? The top searched job categories for Data Optimisation jobs in Ontario are:
Infographic showing various Data Optimisation job openings in Ontario as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 13% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

CA$64K - CA$114K/yr

Full-time

Medical, Dental, Retirement, PTO

Re-posted 7 hours ago


Job description

Are you driven by data, inspired by innovation, and eager to shape the future of insurance?

Join a fast-growing Canadian P&C insurer where you'll play a pivotal role in advancing our Actuarial Services function. We're building on a solid foundation, powered by cutting-edge technology, a robust enterprise data analytics team, and bold investments in AI. With this groundwork in place our talented team is now focused on scaling and refining our capabilities.

We're looking for bright, analytical minds to help bring our strategy to life. If you're passionate about solving complex problems and want to explore Pricing, Underwriting, and Claims, we want to hear from you.

Data Scientist

We're looking for a Data Scientist to join our growing team and build the next generation of predictive analytics solutions. In this role, you won't just be crunching numbers, you'll be using machine learning (ML) and artificial intelligence (AI) models to transform raw data into powerful insights that drive our business forward.

This is an opportunity to directly contribute to our core operations, working alongside a supportive team who are invested in your growth. If you have a passion for data and a desire to see your work make a tangible impact, this role is for you.

What you'll do:

Deliver value through algorithm/model driven insight from data: 

  • Working knowledge of algorithm types and their applications (classification, regression, clustering, etc.)
  • Familiarity with modelling architectures and their strengths/limitations (e.g., gradient boosting, clustering, SHAP, LLMs)
  • Experience applying ML/AI algorithms in a business context under guidance from senior team member
  • Basic understanding of optimization concepts (linear, integer, dynamic programming)
  • Experience in design and practical application of optimization solutions in a business setting given certain constraint 

 Generate data ingestion, model deployment and validation

  • Pull data from various systems using SQL, PySpark, and other standard tools for relational and distributed databases
  • Assist engineering partners in building automated data pipelines for model delivery
  • Support deployment of machine learning models into production environments, including CI/CD workflows and MLOps practices under supervision

Create models through advanced data feature engineering

  • Work with feature stores and operational systems to support automated feature development (MLflow, Databricks, etc.)
  • Apply feature transformation techniques to improve model performance across algorithm types

Integration of machine learning and AI algorithms into core claims functions

  • Basic understanding of actuarial approaches for loss and pricing estimation (GLM, GAM, Tweedie)
  • Familiarity with pricing, reserving, and regulatory functions in P&C insurance
  • Exposure to actuarial or insurance decisionsupport tools (Earnix, Guidewire) is an asset
  • Assist in designing prototypes, analyses, and visualizations incorporating ML/AI models

 Interact with business stakeholders to ensure value and validity of proposed solutions

  • Gather business requirements with support from senior team members
  • Help assess the validity of algorithmic solutions relative to business constraints
  • Communicate results clearly in written and verbal formats

 What You'll Need to Succeed

  • Bachelor's degree required and nice to have a  Masters' or PhD  a quantitative field (Statistics, CS, Mathematics, Actuarial Science, Economics, or similar)
  • 1-3 years in a ML/AI development role ,building and deploying ML/AI models in production
  • Strong hands-on experience with both classical ML (GLMs, LightGBM, XGBoost etc ) and modern NLP (BERT, embeddings, LLMs, Hugging Face, RAG) for modelling both structured & unstructured datasets(tabular, text, document and images)
  • Strong Python & SQL skills and familiarity with cloud/ML platforms (Databricks, Azure)
  • Experience with model governance, MLOps, performance monitoring.
  • Prior experience in P&C insurance pricing, claims, underwriting, or fraud is strongly preferred
  • Excellent analytical, quantitative, and critical thinking skills. Strong business judgment for choosing the simplest model that solves the problem and knowing when to reach for more sophisticated approaches.
  • Outstanding communication and collaboration skills to engage stakeholders, frame ambiguous problems, and translate technical results into business decisions.
  • Knowledge of financial theory or insurance modelling is an asset

If you're ready to be a core part of our mission and make a tangible impact from day one, we want to hear from you.

#LI-Hybrid

The expected base salary range for this position is $64,500 - $114,500. Depending on your relevant experience, skills, qualifications, market conditions and business needs, base compensation may vary. You have the potential to earn more through Gore Mutual's discretionary bonus program which gives you an opportunity to increase your total compensation, provided the business meets its performance targets and you meet your individual goals.

Please note: This range reflects the expected base salary for this role but may not represent the full compensation range for all experience and skill levels. During the recruitment process, we will discuss and consider how your unique qualifications align with the broader range for this position.

Gore Mutual is proud to offer a comprehensive total rewards package which includes extended health and dental benefits, disability insurance, retirement plan matching, paid time off, recognition and perk programs.