Junior Data Scientist - Vancouver, Canada - Theory and Practice

Description

Description

The Junior Data Scientist will join our growing group of AI specialists and software engineers at Theory+Practice working closely with client business specialists to systematize data driven insights and decision making.

If you have a passion for leveraging new technologies and methods to solve complex problems, and love to be in an environment that fosters growth while being part of a great team we invite you to consider joining our team


Responsibilities

• Build pragmatic, scalable and rigorous ML and AI solutions for TAP customers that enable data driven improvements for businesses such as recommendation engines, opportunity scoring frameworks, customer intent models, etc.
• Understanding business objectives and how to achieve them through data driven solutions
• ML models or analytical solutions
• Deliver effective business solutions from ideation to QA and deployment
• Work collaboratively with both internal teams (data engineers, ML engineers, project managers) and clients to define problem statements, collect data and design solutions
• Build and maintain ML models, experiments, and forecasting analytics
• Leverage Python, Hadoop, Spark and similar Big Data frameworks to deliver efficient analytics
• Clearly communicate the methods, impact and processes taken with clients and other stakeholders
• Lead and support junior data scientists in their projects and technical development
• Coordinate with the management to identify key strength of TAP to transform our expertise and best practices into product offerings

Qualifications

• Degree in a STEM field (e.g. Computer Science, Engineering, Physics, Mathematics, Statistics, Economics, or related field)
• Strong working knowledge of probability and statistics
• 2 years of industry or academic experience solving analytical problems
• Experience performing data extraction, data cleaning, exploratory data analysis and sharing results over medium to large datasets
• Strong Python (NumPy, SciPy, Pandas, Scikitlearn etc.) and SQL skills
• Strong data analytics skills (e.g. matplotlib, seaborn, plotly)
• Good communication skills to explain insights and methods
• Excellent analytical skills to selfassess robustness and performance of machine learning models
• Enjoy learning new data science methods and technologies
• Preferred: cloud platforms like AWS, GCP, Azure and their data science stack like Dataproc, PySpark, Cassandra, Redshift, BigQuery, EKS/GKE and other functional open source tools like DVC, Airflow
• Preferred: Experience with data analytics tools like Tableau, Looker, Power BI
• Preferred: Experience with MLOps relevant to different parts like data engineering, model scaling and model deployment
• Preferred: Experience with one or more of Tensorflow, Keras, Theano or Pytorch
• Preferred: Experience in understanding business problems and building machine learning models and analytical solutions to these problems