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Data Scientist

Capgemini

Location

Mississauga, Ontario, Canada

Salary

Not specified

Type

fulltime

Posted

Today

via linkedin

Job Description

Role: Data Scientist

Location: Mississauga, ON

Employment type: Fulltime Permanent

Experience Required:

5-7 Years

Role Overview:

We are seeking a highly skilled and experienced Data Scientistto design, build, and deploy scalable machine learning solutions. The ideal candidate will have strong expertise in Python, advanced SQL, and a deep understanding of statistical modeling and machine learning techniques, with proven experience delivering end-to-end data science solutions.

Key Responsibilities:

  • Lead end-to-end model development: from problem definition to production deployment
  • Develop, validate, and optimize machine learning models for business use cases
  • Perform data analysis, feature engineering, and model evaluation
  • Design and execute A/B tests and experiments
  • Collaborate with cross-functional teams to translate business requirements into data science solutions
  • Ensure model performance, scalability, and reliability in production environments
  • Optimize data workflows and improve query performance

Required Skills:

  • Programming \& Data Science
  • Expert-level proficiency in Python
  • Strong experience with:
  • Pandas, NumPy, SciPy
  • Scikit-learn, Statsmodels
  • Matplotlib, Seaborn
  • XGBoost, LightGBM
  • TensorFlow and/or PyTorch
  • SQL \& Data Engineering

Advanced SQL expertise including:

  • Complex joins, CTEs, and window functions
  • Query optimization and performance tuning
  • Experience in data modeling and schema design
  • Core Data Science Knowledge

Strong understanding of:

  • Statistics and hypothesis testing
  • Machine Learning \& Predictive Modeling
  • Feature Engineering
  • Model Validation \& Performance Optimization
  • Experimental Design and A/B Testing
  • Preferred / Nice-to-Have Skills

Experience in:

  • Natural Language Processing (NLP)
  • Time Series Forecasting
  • Recommendation Systems

Exposure to:

  • Generative AI / LLM applications
  • MLOps and model deployment frameworks
  • Visualization tools:
  • Power BI or Tableau
  • Cloud platforms:
  • Azure ML, AWS SageMaker, or GCP Vertex AI

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