The IoT Industry Solutions team is "start-up" within TELUS that is focused on building end-to-end customer solutions that combine the strengths of our networks, leading SaaS and platform partnerships, our breadth of customer relationships, and the power of data insights.
We own the success of focused vertical and horizontal IoT solutions across Intelligent Mobility, Connected Workers, Smart City, Agriculture, and Health.
We're passionate about building new business areas for TELUS and will succeed by bringing clarity and focus to the challenge.
About the team
This role is part of the Data and Applied Intelligence team within IoT Industry Solutions. We are a provider of data, insights and data science services to commercial and government organizations.
Our product solves many business problems across a wide variety of industries. They are currently being used in Tourism, Commercial Real Estate, Transportation and many other industries to help decision makers plan their next course of action or analyze their historical activities.
We are a fun group of individuals, who are really passionate about our products and committed to provide quality solutions to our customers.
About the role
Design and build large and complex data sets, from spurious sources while thinking strategically about uses of data and how data use interacts with data design
Design and implement statistical data quality procedures for new data sources
Develop algorithms / software for accessing and handling data appropriately
Implement and hand off data checking and updating procedures to teams
Lead the new product developments for the TELUS Insights portfolio and product enhancements for existing location intelligence products
Scale the current custom consulting projects to a generalized product that could be reused for multiple clients across multiple verticals
Develop and implement ML & AI and Big Data solutions including predictive modeling,forecasting and classification
Visualize and report data findings creatively in a variety of visual formats that appropriately provides insights to the organization
Train others on what data is available and how to use various sources
Communicate findings to business leaders in a way that can influence how an organization approaches a business challenge
Support and evolve the TELUS Insights product roadmap by leveraging customer insights, industry research, best practices, and emerging tools / technology
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
You're the missing piece of the puzzle
You have 5+ years of experience working with large temporal geospatial datasets
MSc. or PhD / Research in Computer Science, Statistics, Geospatial etc.
You possess deep expertise in location intelligence / Geospatial data
Working with structured and unstructured data sets
Excellent skills working with complex SQL and Spark
Experience developing in Python; comfortable using various data science libraries such as Scikit-learn, Pandas, Numpy as well as frameworks like TensorFlow, Pytorch, Keras
Experience building and architecting API endpoints
Experience working with telecom background is a plus
Experience working with cloud environments like GCP
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, Classification, Clustering, Segmentation, Time Series Analysis, Demand Forecasting and Optimization
Evaluating and providing input on potential business intelligence solutions.
Comfortable with Jupyter environment and infrastructure, Spyder / PyCharm
Experience with at least one of the major cloud computing platforms - GCP, AWS, Azure
Well versed in software development lifecycle and ML Ops concepts; Developing end-end to models / projects and automation in production environment.
Knowledgeable about common supervised and unsupervised Machine Learning approaches (eg. feature engineering, classification, regression, clustering, NLP, time series forecasting, etc.
Tensorflow , Deep Learning and synthetic tabular data generation is a strong asset
PhD in a quantitative field such as Math, Statistics, Computer Science, Economics, or Data Science
Data visualization experience : Data Studio, Tableau, PowerBI, Domo
Data environments experience : MS SQL, Oracle
Experience using Google Workspace
Experience with agile methodology and team-based software development workflows ( JIRA)