A global Business Process Outsourcing (BPO) company, partnering with a USA Client in the Healthcare Industry, are in search of a Machine Learning Developer / Engineer to join their rapidly expanding team, working remotely.

PLEASE NOTE THE FOLLOWING:

• Working Hours: This role requires you to work USA hours (EST), Mon - Fri, from 9:00am to 6:00pm EST time (3pm to midnight South African time. NOTE: These hours are subject to change depending on daylight savings and/or the operational requirements of the company.)
• Work Environment: This is a remote role for South African Citizens only.
• Internet Requirements: A fixed fibre line with a minimum speed of 25 Mbps (upload & download) and the ability to support a wired Ethernet connection is mandatory. Applicants without a fixed fibre line cannot be considered.
• Power Backup: A reliable power backup solution is required to manage load shedding and power outages. Applicants without a power backup cannot be considered.

Requirements:

Skills:

• Strong problem-solving and coding skills, more than just a programmer.
• Experience building machine learning models.

Ideally have experience with:

• Random Forest, Gradient Boosting, AutoML.
• Performing well on Kaggle machine learning competitions (advantageous).
• 1-2 years of relevant experience.
• Python skills for data analysis and building dashboards with libraries like Dash, Streamlit, Panel, Bokeh.
• Actuarial experience would be an advantage.

Ideal Candidate Profile:

• Background as an engineer or data scientist, ideally with healthcare experience.
• Able to discuss specific models built, methodologies used, and feature engineering approaches.

Duties and responsibilities:

• Develop and implement machine learning models to solve complex business problems from the ground up.
• Use algorithms such as Random Forest, Gradient Boosting, and AutoML to enhance model performance.
• Ensure models are scalable and maintainable.
• Perform detailed data analysis to extract meaningful insights.
• Conduct feature engineering to improve model accuracy.
• Validate and clean data to ensure high-quality datasets for model training.
• Communicate findings and recommendations to stakeholders in a clear and concise manner.
• Collaborate with team members to integrate models into existing systems and workflows.
• Create dashboards and visualizations using Python libraries such as Dash, Streamlit, Panel, and Bokeh.
• Present data-driven insights through interactive and user-friendly dashboards.
• Provide regular reports on model performance and business impact.
• Apply machine learning techniques to healthcare-specific problems.