Data Scientist
Posted on 2/17/2024
INACTIVE
Ziing

11-50 employees

Efficient last-mile delivery & route optimization
Company Overview
Ziing distinguishes itself in the last-mile delivery market through its data-driven platform that optimizes route planning and scheduling, enabling cost-effective and tailored logistics solutions for businesses. The company's commitment to responsible delivery is supported by a robust national network of local partners and a 24/7 operating team, ensuring reliable and competitive service. Ziing's approach to blending dedicated and distribution services reflects a flexible and customer-centric work culture, making it an attractive workplace for those interested in contributing to a dynamic and responsive logistics environment.
Food & Agriculture
Data & Analytics
Consumer Goods

Company Stage

N/A

Total Funding

N/A

Founded

2018

Headquarters

Calgary, Canada

Growth & Insights
Headcount

6 month growth

30%

1 year growth

50%

2 year growth

41%
Locations
Calgary, AB, Canada
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Python
Data Science
R
Tableau
Data Analysis
CategoriesNew
Data Science
Data Analysis
Data & Analytics
Requirements
  • Bachelor's, Master's, or PhD degree in Computer Science, Statistics, Mathematics, or a related field.
  • 3+ years of experience as a data scientist or machine learning engineer.
  • Strong programming skills in Python or R.
  • Experience with data analysis and statistical modeling.
  • Familiarity with machine learning frameworks such as TensorFlow or PyTorch.
  • Experience with data visualization tools such as Tableau or D3.js.
  • Strong communication skills and ability to present complex findings to non-technical stakeholders.
  • Ability to work collaboratively in a fast-paced, cross-functional environment.
  • MLOPs experience is a plus.
  • Experience with LLMs is a plus.
  • Prefer candidates based out of Calgary, Edmonton, Vancouver, or Toronto.
Responsibilities
  • Analyze complex data sets and develop predictive models.
  • Collaborate with product managers, designers, and engineers to define data-driven product requirements and ensure data quality.
  • Design and build machine learning models to extract insights and improve user experience.
  • Develop data visualizations to communicate findings to stakeholders.
  • Conduct statistical analysis and hypothesis testing to validate hypotheses and evaluate product performance.
  • Build data pipelines and automate data analysis workflows.
  • Continuously monitor and improve model performance to ensure accuracy and efficiency.
  • Develop data-driven metrics to evaluate product performance and identify areas for improvement.
  • Keep up to date with the latest advances in machine learning and data science techniques.