Junior Data Scientist

Bevertec; Toronto; Hybrid; CAD 69,000 - 76,000; Full time

Job summary

Bevertec in Toronto is seeking a data-focused professional for a hybrid role, combining on-site collaboration and remote work.

You will apply Python, SQL and ML concepts to analyze data, build models, and support business decisions. The team values clear communication and fast learning in a dynamic tech environment.

The position offers collaborative work with onsite days and flexible options, emphasizing teamwork and data-driven insights.

Qualifications

• Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or related field, or equivalent experience.
• 0–3 years of relevant experience, including internships, academic projects, or professional experience.
• Strong Python programming skills for data analysis and machine learning.
• Understanding of supervised and unsupervised machine learning concepts.
• Knowledge of SQL and data extraction and manipulation.
• Familiarity with data science libraries, statistical methods, and model evaluation techniques.
• Understanding of structured and unstructured data.
• Strong analytical, problem-solving, communication, and learning abilities.

Skills

Python

SQL

Data analysis

Machine learning

Communication

Learning agility

Education

Bachelor's degree

Tools

LangChain

LangGraph

CrewAI

PySpark

SageMaker

Job description

Ideal Rate: $50- $55 (Based on Experience)

Work Arrangement: Hybrid – 3 days onsite per week, including Thursday

Required Qualifications

• Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a related field, or equivalent experience.
• 0–3 years of relevant experience, including internships, academic projects, or professional experience.
• Strong Python programming skills for data analysis and machine learning.
• Understanding of supervised and unsupervised machine learning concepts.
• Knowledge of SQL and data extraction and manipulation.
• Familiarity with data science libraries, statistical methods, and model evaluation techniques.
• Understanding of structured and unstructured data.
• Strong analytical, problem-solving, communication, and learning abilities.

Nice to Have

• Academic, internship, or personal projects involving machine learning or data science.
• Exposure to Generative AI, LLMs, prompt engineering, RAG, or chatbot development.
• Familiarity with frameworks such as LangChain, LangGraph, or CrewAI.
• Exposure to PySpark, APIs, or cloud technologies.
• Familiarity with AWS AI/ML services such as SageMaker or Bedrock.

Note: AI-enabled tools may be used to sort applications based on job-related criteria. All AI generated results are vetted by our team and the decision of which candidates move forward is always made by a human.