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.