Job Specifications
Position Title: Machine Learning Professional / Data Scientist
Duration: 12 Months (Possible Extension)
Location: Toronto, ON (Hybrid – 2–3 days onsite per week)
Position Overview
We are seeking an intermediate-level AI/ML Professional / Data Scientist to support end-to-end delivery of predictive, quantitative machine learning solutions. This role focuses on traditional machine learning and statistical modeling, not GenAI, LLMs, or NLP-heavy use cases.
The ideal candidate will work closely with business stakeholders, data engineers, and MLOps teams to translate business requirements into scalable, production-ready ML models that drive data-driven decision making.
Important Notes
This role is not focused on GenAI, LLMs, or prompt engineering
The emphasis is on predictive, quantitative, and statistical machine learning models
Candidates may come from any industry; banking experience is a plus but not required
Key Responsibilities
Partner with business stakeholders to gather requirements and translate business problems into analytical and machine learning solutions
Collaborate with data engineers on data ingestion, ETL pipelines, and preparation of large-scale datasets
Perform exploratory data analysis (EDA), feature engineering, data manipulation, and preprocessing using Python libraries such as Pandas, NumPy, and SciPy
Design, develop, and implement traditional machine learning models including classification, regression, clustering, and other predictive techniques
Validate, test, and evaluate model performance to ensure accuracy, stability, and business relevance
Document models, assumptions, methodologies, and results for technical and non-technical audiences
Work with MLOps and cloud teams to deploy, monitor, and maintain models in production
Analyze large and complex datasets to identify patterns, trends, and actionable insights
Provide data-driven recommendations to support strategic and operational decision making
Contribute to continuous improvement of analytics processes, tools, and best practices
Required Skills & Experience
2+ years of experience building and deploying machine learning models in a production environment
2+ years of experience working with large datasets, including data ingestion, processing, merging, and aggregation
Strong programming skills in Python and SQL (SAS is an asset)
Solid understanding of statistics, mathematics, and quantitative modeling techniques
Hands-on experience with data wrangling, preprocessing, and feature engineering
Strong problem-solving, analytical, and critical-thinking skills
Excellent verbal and written communication skills, with the ability to engage business stakeholders
Ability to work independently and manage non-routine analytical problems
Preferred / Nice-to-Have Qualifications
Experience with pricing or revenue modeling
Exposure to Banking, Insurance, or Financial Services domains (not mandatory)
Experience with big data platforms and data visualization tools
Familiarity with cloud platforms and MLOps workflows (e.g., model deployment and monitoring)
Experience with Python-based ML frameworks and cloud services (e.g., SageMaker is an asset)
Knowledge of trust, bias, ethics, and responsible AI practices
About the Company
Welcome to SPECTRAFORCE, your gateway to NEWJOBPHORIA(tm)!
Established in 2004, SPECTRAFORCE is now one of the largest and fastest growing U.S. staffing firms renowned for its exceptional client service, SPECTRAFORCE's innovative A.I.-powered talent acquisition platform and proven methodologies set us apart in the industry.
We offer a comprehensive range of services including Contingent, Permanent, and Statement of Work (SOW) staffing solutions. Our expertise extends across multiple sectors such as Technology, Financial Se...
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