Data Scientist Ml

1 week ago


Mississauga, Ontario, Canada BioTwin Full time

About us
BioTwin leverages AI, biomarkers and biometrics to create a virtual human copy. These virtual twins enable early detection and prediction of diseases and countless simulations. This technology is revolutionizing the fields of wellness and healthcare by making personalized and preventive medicine a reality.

About the job
We are seeking a Data Scientist with an expertise in Machine Learning to join our dynamic Startup team.


Reporting to the Twin Technology Team Lead, the Data Scientist, will be instrumental in driving data-driven decision-making and developing cutting-edge machine learning models to solve complex business problems.

Your proficiency in data analysis, predictive modeling, and algorithm development will be pivotal in uncovering valuable insights and empowering our Digital health Startup to thrive in a competitive market.


Here are more details on his roles and responsibilities:

Lead the design and development of ML/DL models and software solutions

  • Develop a clear understanding of project requirements, collaborate with crossfunctional teams, and lead the design and implementation of ML models and software solutions for digital healthcare. _
  • Continuously monitor and improve the performance of ML models and software solutions.

Collaborate with cross-functional teams

  • Engage with stakeholders, understand their needs, and work with crossfunctional teams to translate these needs into ML and software solutions. _
  • Actively listen to stakeholders, foster open communication, and engage in collaborative problemsolving to arrive at the best solutions.

Ensure ML models and software solutions are production-ready, secure, scalable, explainable, and maintainable.

  • Implement and maintain best practices in software engineering and ML development, perform thorough testing, and continuously monitor and improve the performance of ML models and software solutions_
  • Continuously monitor and improve the performance of ML models and software solutions.

Monitor and improve model and software performance.

  • Continuously monitor the performance of ML models and software solutions, identify areas for improvement, and implement changes to improve performance._
  • Regularly evaluate the performance of ML models and software solutions
  • Identify areas for improvement
  • Implement changes to improve performance.

Mentor and guide junior ML engineers and junior software engineers in the team.

  • Share knowledge and expertise, provide guidance, and support junior team members as they develop their skills and knowledge_.
  • Actively engage with junior team members, provide guidance and support.
  • Foster an environment of continuous learning and growth.

Stay current with advancements in ML, software engineering and the digital healthcare industry.

  • Continuously learn and stay informed of advancements in ML, software engineering, and the digital healthcare industry_.
  • Regularly attend conferences, workshops, and training sessions
  • Read industry publications and books,
  • Stay engaged with the broader ML and software engineering communities.

Lead technical discussions and decisions related to ML and software engineering.

  • Lead technical discussions and decisionmaking processes related to ML and software development and provide expert guidance to the team and recommendations to management._
  • Actively engage with the team
  • Provide expert guidance and advice
  • Facilitate informed and inclusive decisionmaking processes.

Communicate project progress, technical challenges, and solutions to stakeholders.

  • Regularly update stakeholders on project progress, technical challenges, and solutions._
  • Maintain open and transparent communication with stakeholders
  • Provide regular updates
  • Actively engage in discussions to arrive at solutions.

Post Challenge:

  • Rapidly changing technology:_ The field of ML and digital health is rapidly evolving, with new techniques and technologies emerging all the time. Keeping up with these changes and incorporating them into BioTwin's products and services can be a significant challenge.
  • Privacy and security:_ Digital health startups, such as BioTwin, often handle sensitive patient data, which requires compliance with various privacy and security regulations. Ensuring that the company's ML systems and software comply with these regulations is a significant challenge.
  • Balancing speed and quality:_ Startups in digital health, such as BioTwin, often operate in fastpaced environments where the pressure to deliver results quickly can be high. Tech Lead ML Engineers need to be able to balance the need for speed with the need to deliver highquality, productionready solutions that are secure, scalable, explainable and maintainable.

Required:

  • A bachelor's or master's degree in computer science, electrical engineering, or a related field is typically required, with a focus on machine learning and artificial intelligence.

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