Junior Data Scientist

Job ID 2021-1763


iQuartic is a dynamic and growing company that helps customers solve critical healthcare problems and empower stakeholders with reliable, actionable intelligence using state-of-the-art data science and AI solutions.


Our diverse and compassionate team brings deep experience in healthcare and technology to lead the healthcare revolution and bring innovative and high value care using intelligence from data.


The Data Scientist role is a blend of hands-on development and architecture design data science work, supporting our product, sales, and marketing teams.


The Data Scientist will use the latest AI/Machine Learning techniques and large data sets to support and transform our product, product roadmap, and process optimization and automation. The role encompasses using a variety of data mining/data analysis methods, using a variety of data tools, building and implementing models, using/creating algorithms and creating/running prototypes, MVPs, and products, and building solutions hidden in large data sets and working with stakeholders to improve business outcomes.


The new Data Scientist will be part of the Global Data Science and AI Team, based in our Montreal headquarter. You will report to the Vice President of Technology. 


  • The Data Scientist will work closely with the data science team and domain experts at IQuartic, leverage OCR, machine learning, deep learning, transfer learning, computer vision, and NLP techniques to develop digitization software for medical record scans.
  • Transform our ICD Extraction NLP model, Visit Recognition NLP model, etc
  • Be part of a highly energized team where you will import the latest AI/Machine Learning techniques from industry and academia, design and implement software solutions for a broad range of NLP/CV problems typically found in the healthcare domain.
  • Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
  • Drive company AI strategy, product development, and product roadmap.
  • Develop custom data models and algorithms to apply to data sets.
  • Use predictive modeling to increase and optimize customer experiences, revenue generation, and other business outcomes.
  • Develop company A/B testing framework and test model quality.
  • Develop processes and tools to monitor and analyze machine learning model performance and data accuracy.


Advanced degree in a quantitative discipline, e.g., Mathematics, Linguistics, Computer Science.


>2 years industry or academic experience in applied NLP, CV, DL, TL.


Excellent verbal and written communication, interpersonal skills, and team collaboration.


Specialization in OCR is strongly preferred. Understanding of Transformers, ELMO, XLNet, Albert, BERT is preferred but not required.


Experiences with healthcare industry practices and medical coding a plus, but not required.


Demonstrates proficiency with:

  • Python, Scala, Java, or R for product development and to draw insights from large data sets.
  • OCR libraries such as Tesseract, PyOCR, OpenCV, .NET OCR SDK, etc.
  • NumPy and Pandas libraries for extracting, cleaning, transforming, and preprocessing data sets.
  • Supervised and unsupervised machine learning techniques. This includes regression models, decision tree models, clustering, and deep learning. Hands-on experience with Scikit-learn, TensorFlow, Keras, or PyTorch.
  • Data visualization and performing model diagnostics. Understand learning curves, work with tools such as Matplotlib, Tableau, etc.
  • Experiences using NLTK, spaCy, Stanford NLP, Open NLP, or Spark NLP.
  • Experience with Amazon Web Services, Microsoft Azure, or Google Cloud Platform services.
  • Strong problem-solving skills with an emphasis on applied research and product development.
  • Experience using statistical computer languages (R, Python, SQL, etc.).
  • Experience working with and creating data and ML models architectures.
  • Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, transfer learning, GAN) and their real-world advantages/drawbacks.
  • A drive to explore, research, learn, and master new technologies and techniques.
  • Experience using web services: S3, Spark, MongoDB, Redshift, KEDA, Kafka, Flask, MLflow, Kubeflow, etc.



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