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.
Method of Application
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