Machine Learning Engineer

Helium Health is a full-service Healthtech company that provides a suite of solutions for healthcare providers, payers, and patients in emerging markets - at the core of which is our SaaS-based electronic medical records/hospital management information system (EMR/HMIS). 

The Machine Learning Engineer will play a crucial role in designing, developing, and deploying state-of-the-art Machine Learning models and algorithms. S/he will collaborate with cross-functional teams to drive innovation, solve complex business challenges, and deliver scalable ML solutions. The ideal candidate must possess deep technical expertise in Machine Learning, strong programming skills, and a proven track record of delivering successful ML projects.

Responsibilities:

Research and Development:

• Conduct in-depth research on cutting-edge Machine Learning techniques and stay abreast of the latest advancements in the field.

• Explore and experiment with novel ML algorithms, models, and frameworks to address specific business challenges.

• Design, implement, and optimize Machine Learning models and algorithms to deliver robust and scalable solutions for various applications.

• Leverage data preprocessing techniques and feature engineering to enhance model performance and accuracy.

Data Management:

• Collaborate with data engineers and data scientists to ensure seamless data integration, data quality, and data pipeline efficiency.

• Work with large datasets, both structured and unstructured, to extract valuable insights and patterns.

• Develop robust testing frameworks and methodologies to evaluate the performance of ML models in real-world scenarios.

• Implement A/B testing and statistical analysis to validate the effectiveness of deployed models.

Deployment and Scaling:

• Deploy Machine Learning models into production environments, considering scalability and maintainability.

• Monitor model performance, conduct periodic updates, and troubleshoot issues to ensure optimal system functionality.

• Collaborate with cross-functional teams, including data scientists, software engineers, and product managers, to drive ML initiatives and achieve project goals.

• Provide technical leadership and mentorship to junior members of the ML engineering team.

Documentation and Communication:

• Document all development processes, methodologies, and experimental results to facilitate knowledge sharing and future enhancements.

• Communicate complex technical concepts and findings to both technical and non-technical stakeholders effectively.

• Perform other duties as assigned.

Requirement:

• Bachelor's or master's in computer science, Data Science, Machine Learning, or a related field.

• 5+ years of industry experience as a Machine Learning Engineer, developing and deploying ML models in real-world applications.

• Expert knowledge with a scripting language (e.g. Python) and with an object-oriented language (e.g. C++, Java).

• Strong understanding of Machine Learning algorithms, statistical modeling, and optimization techniques.

• Proficiency in the development, validation, implementation, and production launch of machine-learning algorithms and models.

• A passion for implementing coding best practices across a team.

• Strong leadership skills. Must be able to define and direct the scope of work of Junior team members.

• Experience with popular ML libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).

• Solid knowledge of data processing, feature extraction, and data visualization techniques.

• Ability to create logical data models by combining data from multiple sources including internal, and external data.

• Ability to test ideas and adapt methods quickly end to end from data extraction to implementation and validation.

• Familiarity with cloud computing platforms and distributed systems for scalable ML deployments.

• Excellent problem-solving, analytical, and communication skills.

• Ability to work collaboratively in a fast-paced and dynamic environment.

• Deep appreciation for diversity of thought and a proponent for collaborative solutions.

• Ability to communicate technical concepts and solutions at a level appropriate for technical and non-technical audiences.