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Junior Data Scientist

3 weeks ago

Vancouver, Canada Theory and Practice Full time

**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, Scikit-learn 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 self-assess 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