L4 Data Management
Calgary, AB, CA Edmonton, AB, CA Victoria, BC, CA Vancouver, BC, CA Toronto, ON, CA
Description
Ready to create innovative solutions and best practices?
Our team and what we’ll accomplish together
As an experienced professional in the field of data science, you will be part of the journey that brings us to a better understanding of our customers in order to predict their needs and interaction behaviour.
With a natural curiosity to experiment, and expert knowledge of AI & ML concepts, you’ll perform hypothesis, validation and develop models through an iterative approach in order to predict customer demand and behaviours.
You will be part of a team that supports your personal growth with progressive training and development while also providing opportunities for you to showcase your abilities by contributing to high-value and high-visibility projects.
What you’ll do
- Develop and implement ML & AI and Big Data solutions including predictive modelling, customer impact assessments, etc.
- Execute, oversee, and evolve models and algorithms selection to deliver solutions that are relevant and facilitate decision making
- Build and maintain a robust interlock with key stakeholders to understand business needs and priorities
- Identify opportunities for process/model optimization and refine to improve effectiveness/accuracy and enhance ROI
- Collaborate with Data Scientists and Data Engineers within TELUS as well as external Data Science communities
Qualifications
What you bring
- You are recognized for addressing business needs via your application of data mining and analysis,predictive modeling, statistics, and other advanced analytical techniques
- You are sought out for your skills in Machine Learning, Regression, Classification, Clustering,Segmentation, Time Series Analysis, Demand Forecasting and Optimization
- Experience developing in Python; comfortable using various data science libraries such as Scikit-learn,Pandas, Numpy as well as frameworks like Pytorch or Keras
- Comfortable with Jupyter environment and infrastructure
- Proficiency with SQL
- Experience with at least one of the major cloud computing platforms - GCP, AWS, Azure
- Well versed in software development lifecycle and ML Ops concepts
Great-to-haves
- Degree in a quantitative field such as Math, Statistics, Computer Science, Economics, or Data Science
- Data visualization experience: Data Studio, Tableau, PowerBI
- Data environments experience: MS SQL, Oracle
- Experience with Virtual machines, BigQuery and other Google Cloud Platform services
- Experience with agile methodology and team-based software development workflows (e.g. JIRA)