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Tek Tron IT

Data Scientist with Machine Learning Engineer

Tek Tron IT
Canada · Full-time · Entry
Key Responsibilities

Data Science & Analytics

  • Explore, clean, and analyze structured and unstructured datasets.
  • Apply statistical techniques to generate insights and support business decisions.
  • Develop predictive models using regression, classification, clustering, NLP, or time-series methods.
  • Conduct hypothesis testing, A/B experiments, and exploratory data analysis (EDA).

Machine Learning Engineering

  • Build, train, and fine-tune ML models using frameworks such as TensorFlow, PyTorch, Scikit-learn.
  • Develop and maintain end-to-end ML pipelines, including feature engineering, model training, evaluation, and versioning.
  • Deploy ML models into production using MLOps pipelines and tools like MLflow, Kubeflow, SageMaker, or Vertex AI.
  • Implement monitoring, retraining strategies, and improve model performance.
  • Optimize models for scalability, latency, and resource efficiency.
  • Requirements

Must-Have

  • Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, AI/ML, or related field.
  • 3–7+ years of experience as a Data Scientist, ML Engineer, or similar role.
  • Strong programming skills in Python (NumPy, Pandas, Scikit-learn, Matplotlib, etc.).
  • Experience with ML frameworks (TensorFlow, PyTorch).
  • Strong understanding of machine learning algorithms, statistics, and data modeling.
  • Experience deploying models in production (Docker, APIs, cloud deployment).
  • Knowledge of SQL and experience with relational and NoSQL databases.
  • Familiarity with MLOps tools (MLflow, Kubeflow, Airflow, DVC)

Key Skills

Ranked by relevance

tensorflow kubeflow pytorch mlflow mlops machine learning data analysis matplotlib python docker pandas nosql cloud numpy sql ai
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Country Guide

Canada

Express Entry & tech-friendly immigration

Posted
Dec 03, 2025
Type
Full-time
Level
Entry
Location
Canada
Company
Tek Tron IT

Industries

IT Services IT Consulting

Categories

Engineering Information Technology

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