Data Scientist

1 week ago

Greater Toronto Area, Canada SPECTRAFORCE Full time

Position Title:
AI/ML 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

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