Data Scientist

At Gore Mutual, we’ve always set ourselves apart as a modern mutual that does good. Now, we’re proudly building on that legacy to transform our company—and our industry—for the better.

Effective January 1, 2026, Gore has joined Beneva—the country’s largest mutual insurance company—as part of its Property & Casualty operations in Ontario and Western Canada. During 2026, Gore will combine its operations with Unica Insurance, Beneva’s Ontario-based subsidiary specializing in niche commercial and personal insurance, creating a stronger, more diversified mutual insurer with greater scale and long‑term stability.

Every decision and investment remains anchored in long‑term benefits to customers, members, and communities. Come join us.

Our Data Science practice at Gore Mutual Insurance is expanding to accelerate our move towards becoming a truly data‑driven and digitally led company. To continue our journey forward, we are looking to onboard Data Scientist (two roles) to develop efficient, reusable pipelines and algorithms to drive value from data. This role will be responsible for development of efficient reusable pipelines to ingest data, construct features, develop algorithms, and deploy models for driving value from data.

What will you do? Construct pipelines for algorithm / model driven insight from data

Understanding of different algorithm types (supervised classification, regression, unsupervised, reinforcement etc.)

Understanding of different modelling architectures, strengths and weaknesses (e.g., gradient boosting, clustering, SHAP, LLMs, autoencoders)

Experience in design and practical application of AI algorithms in a business setting.

Construct pipelines for optimization of business solutions given known constraints

Understanding of different optimization techniques (linear optimization, integer optimization, dynamic programming etc.)

Experience in design and practical application of optimization solutions in a business setting

Construct pipelines for data ingestion and model deployment and validation

Pull data from various systems using SQL, PySpark, and other standard languages for relational and distributed databases and set up data ingestion pipelines to third‑party sources via API etc.

Work with engineering partners to construct automated data pipelines for continuous delivery of data to models.

Deploy machine learning models into production environments (e.g., implementing continuous integration and delivery (CI/CD) pipelines for automated model deployment, applying MLOps practices to maintain the lifecycle of machine learning models through testing and validation).

Construct pipelines for feature engineering of data

Work with Ops‑based systems for feature stores / automation of feature development (MLFlow, Databricks, etc.)

Understand feature transformation techniques to extract maximal value from data with respect to different algorithm types.

Interact with business stakeholders to ensure validity of proposed solutions

Communication with business stakeholders to understand requirements.

Understand validity of algorithmic & optimization solutions with respect to business constraints and value.

Clear communication, both written and oral, for dissemination of results to business stakeholders.

What will you need to succeed in this role?

Bachelor’s degree in computer science or equivalent (MS or PhD advantageous but not required). Require 1‑3 years of experience in the skills below:

Strong coding experience with Python and familiarity with machine learning packages and libraries (e.g., TensorFlow/Keras, Scikit‑Learn, PyTorch/FastAI).

Familiarity with cloud technologies (e.g., Azure, MLflow, AWS).

Wide understanding of ML architectures (e.g., GANs, LLMs, Reinforcement Learners).

Strong communication skills to effectively collaborate and present insights with other team members.

Experience leveraging visualization technologies to interpret complex data, create insightful dashboards, and present findings in a clear and impactful manner (e.g., PowerBI, matplotlib, seaborn, plotly, ggplot, geoplotlib).

Experience with deployment of machine learning models into production environments (e.g., implementing continuous integration and delivery (CI/CD) pipelines for automated model deployment, applying MLOps practices to maintain the lifecycle of machine learning models, model monitoring, and model performance metrics).

Strong understanding of concepts around software engineering and computer science.

Literacy in modern financial theory (e.g., risk, pricing, portfolio construction) and/or insurance modelling an asset.

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.

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.

This posting pertains to an existing vacancy that is actively being filled.

Accessibility for applicants Gore Mutual Insurance Company is committed to providing accommodations for people with disabilities during all phases of the recruiting process, including the application process.

If you require accommodation because of a disability, we will work with you to meet your needs. Contact us and a human resources representative will consult with you to determine an appropriate accommodation. If you request an accommodation during the interview process, please notify your Talent Acquisition Consultant.

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