ML Engineer

Toronto, ON

Looking for your next opportunity? Our Client is currently looking for a Machine Learning Engineer to join their team in Toronto: Newfound Recruiting is a Canadian professional services company, headquartered in Ottawa, Ontario and is one of Canada’s leaders in Professional Staffing and Recruiting. Currently, Newfound Recruiting is servicing clients across Canada, having established relationships in Ontario (Toronto and Ottawa), Alberta, British Columbia, Quebec, and Newfoundland. By applying innovative techniques, we match the right candidate with the right position. Our 20 years of industry experience gives you the assurance that we will provide the right answers to your difficult questions. At Newfound, we believe that diversity and inclusion among our teammates is critical to our success, and we seek to recruit, develop, and retain the most talented people from a diverse candidate pool. We are committed to providing an environment of mutual respect where equal employment opportunities are available to all applicants and teammates without regard to age, race, colour, national origin, sex, gender, sexual orientation, religion, physical or mental disability, or any other category protected by law. All employment at Newfound is based on personal merit, qualifications, experience, ability, and job performance. Newfound welcomes and encourages applications from people with disabilities, and we will provide reasonable accommodations, accessible formats, and communication support upon request to persons with disabilities during the recruitment and selection process. If you require accommodation, please contact our Human Resources department at info@newfoundrecruiting.com

Position Overview

We are seeking an experienced Machine Learning Engineer with a strong background in the financial services or banking sector to join our data and analytics team. The ideal candidate will have hands-on expertise in Databricks and modern data engineering workflows, with a proven track record of designing, building, and deploying machine learning models to solve complex business problems such as fraud detection, credit risk assessment, customer segmentation, and transaction analytics.

Key Responsibilities

Model Development & Deployment
- Design, build, and optimize scalable ML models on Databricks for financial services applications.
- Work with structured and unstructured data to address business challenges such as fraud detection, credit scoring, portfolio risk, and compliance monitoring.
- Deploy ML models into production using Databricks MLflow or other CI/CD frameworks.
Data Engineering & Processing
- Ingest, clean, and transform large datasets from multiple banking and financial systems.
- Build and maintain data pipelines leveraging Spark and Databricks for high-performance distributed processing.
- Ensure data governance and compliance with financial regulatory requirements (e.g., GDPR, PCI-DSS, Basel III).
Collaboration & Stakeholder Engagement
- Partner with data scientists, analysts, and business stakeholders to translate business needs into technical solutions.
- Present findings and model outcomes to both technical and non-technical stakeholders in clear, actionable terms.
Continuous Improvement
- Evaluate and implement new tools, techniques, and frameworks to enhance ML capabilities.
- Maintain and improve existing ML models for accuracy, performance, and scalability.

Required Qualifications

Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Engineering, or related field.
Experience:
- 5+ years in Machine Learning or Data Engineering roles.
- 3+ years of hands-on experience with Databricks (Spark, Delta Lake, MLflow).
- Strong knowledge of ML algorithms, statistical modeling, and feature engineering.
- Solid background in the banking/financial sector with experience in domains like credit risk, fraud detection, or regulatory compliance.
Technical Skills:
- Proficiency in Python and/or Scala for ML and data engineering tasks.
- Strong SQL skills for data manipulation and analysis.
- Experience with cloud platforms (AWS, Azure, or GCP), ideally Azure Databricks.
- Familiarity with CI/CD pipelines, containerization (Docker), and orchestration tools (Airflow, Databricks Workflows).

Preferred Qualifications

Experience with MLOps best practices for financial services.
Knowledge of time-series modeling and real-time analytics.
Understanding of banking regulations, compliance, and reporting requirements.
Familiarity with BI and visualization tools (Power BI, Tableau).

Soft Skills

Strong analytical and problem-solving abilities.
Excellent communication skills, with the ability to explain technical concepts to non-technical audiences.
Attention to detail, especially in high-regulation environments.
Ability to work independently and in a cross-functional team.