Lead Machine Learning Engineer

At mDoc, we are revolutionizing health care in low
- and middle-income communities by leveraging AI-driven virtual self-care health coaching, innovative solutions for health literacy, and tools for medication adherence and personalized support. Our mission is to provide holistic care and improve outcomes for individuals managing chronic health conditions and regular health needs. Join our team to be a part of this transformative journey.

**Role Overview**

**Key Responsibilities**
- **Model Development & Deployment**:

- Design, develop, and deploy machine learning models to improve health coaching, personalization, and predictive analytics.
- Implement models for patient risk stratification, medication adherence, adverse event prediction, and health literacy assessments.
- **Data Management & Preprocessing**:

- Collect, clean, and preprocess large datasets from diverse sources, including health metrics, chatbot interactions, and user data.
- Design scalable data pipelines for real-time and batch processing.
- **Collaboration with Cross-Functional Teams**:

- Work closely with clinical, operational, and technical teams to understand business goals and translate them into data-driven solutions.
- Collaborate with product managers and software engineers to integrate machine learning solutions into mDoc’s platform seamlessly.
- **Model Evaluation & Optimization**:

- Evaluate model performance using appropriate metrics and refine models for better accuracy, scalability, and efficiency.
- Conduct A/B testing to measure the impact of machine learning solutions on user engagement and health outcomes.
- **Innovation & Research**:

- Stay updated on the latest advancements in machine learning, AI, and health tech, and propose innovative approaches for implementation.
- Experiment with generative AI, natural language processing (NLP), and reinforcement learning to enhance platform capabilities.
- **System Maintenance & Monitoring**:

- Monitor deployed models and systems for accuracy, performance, and reliability.
- Troubleshoot and resolve issues as they arise.

**Required Skills & Qualifications**
- Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.
- Proven experience in developing and deploying machine learning models in production environments.
- Strong programming skills in Python and familiarity with frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Proficiency in working with SQL and NoSQL databases (e.g., MongoDB).
- Experience with data visualization tools such as Power BI, Tableau, or Matplotlib.
- Familiarity with cloud platforms such as AWS, GCP, or Azure for machine learning workflows.
- Excellent problem-solving skills and the ability to work in a fast-paced, collaborative environment.

**Preferred Skills**
- Experience in the healthcare domain or with digital health platforms.
- Hands-on experience with NLP techniques and tools like SpaCy, Hugging Face, or OpenAI APIs.
- Knowledge of DevOps practices, containerization (Docker), and CI/CD pipelines.
- Strong understanding of data privacy, security, and compliance standards in healthcare (e.g., HIPAA).

**Why Join mDoc?**
- Be part of a mission-driven team transforming healthcare in underserved communities.
- Opportunity to work on cutting-edge AI solutions in a rapidly growing health tech space.
- Competitive compensation and opportunities for professional growth.
- Flexible working environment with a focus on innovation and impact.

Pay: E£25,000.00 - E£50,000.00 per month

Application Question(s):

- Can you describe an ML model you developed and deployed in production? What were the challenges, and how did you address them?
- Can you share an experience where feature engineering significantly improved your model’s performance?
- Which ML frameworks and libraries do you use most frequently? Can you provide examples of how you’ve used TensorFlow, PyTorch, or similar tools?

**Education**:

- Bachelor's (required)

Application Deadline: 27/11/2024