Location:
Remote / Hybrid (Surrey, BC)
Employment Type:
Full-Time (Internship-to-Hire Option Available for Students Nearing Program Completion)
Salary:
Annual salary of $57,000, pro-rated for the duration of the employment term. Salaries are paid on a bi-weekly basis.
Job description
Our ideal candidate's favorite words are learning, data, scale, and agility. You will leverage your strong collaboration skills and ability to harness the power of data to build advanced machine learning models and drive innovation. In this role, you will work with cutting-edge technologies, develop and deploy machine learning algorithms, and contribute to the development of intelligent systems. If you are a creative problem solver with a passion for machine learning and a strong foundation in data science, we invite you to join our team.
Responsibilities
- Data Processing and Extraction: Efficiently handle both structured and unstructured data to pinpoint and retrieve essential information. Harness the capabilities of tools like Tesseract and various OCR libraries for precise text extraction.
- Technological Enhancement: Delve into machine learning/deep learning libraries to enhance the accuracy of extraction, particularly when dealing with intricate files or diagrams.
- Data Organization and Integration: After extraction, methodically organize the data, ensuring it's stored in user-friendly formats. Utilize designated platforms for processing, always prioritizing data integrity and ensuring seamless compatibility with subsequent applications.
- Realization: Collaborate with engineering team to bring analytical prototypes to production.
Skills / Experience / Qualifications
- Bachelor's degree or equivalent experience in quantitative field (Statistics, Mathematics, Computer Science, Engineering, etc.).
- At least 1 – 2 years of experience in quantitative analytics or data modeling.
- Familiarity with cloud computing platforms like AWS, Google Cloud, or Azure
- Deep understanding of predictive modeling, machine-learning, clustering and classification techniques, and algorithms.
- Fluency in a programming language (Python, R, SQL).
- Knowledge of one or more of the following data science toolkits: Pandas, NumPy, Scikit-learn, NLTK, matplotlib, TensorFlow, PySpark, PyTorch.
- Knowledge of image processing and computer vision techniques
- Ability to work with large and complex datasets, and the ability to clean, preprocess, and manage data effectively.
- Attention to detail and problem-solving skills to address data quality issues, outlier detection, and data visualization.
- Ability to work collaboratively in a team environment and contribute to collective goals.
- Capability to handle multiple tasks and prioritize effectively to meet deadlines.
- Willingness to learn and adapt quickly to new technologies and business processes.
- Proactive approach to problem-solving and innovating solutions through data analysis.