Data Scientist

Requirements: Degree in Computer Science or a related field (Master's Degree preferred but not required) Graduate-level mathematical training (e.g., undergraduate degree in a quantitative subject) 3 years of experience in a Data Scientist role or similar, focusing on data analysis, visualization, Machine Learning, and AI Proficient in Python and object-oriented programming principles 3 years of experience with Tableau, PowerBI, or similar dashboard tools 3 years of experience with SQL Strong experience with cloud platforms (AWS, Azure, GCP) and cloud-based data services (e.g., AWS Athena, Redshift, Google BigQuery) Solid understanding of data modeling Experience with version control systems (e.g., Git) Experience delivering Data Science, Machine Learning, and AI projects in production environments using agile methods Ability to present insights effectively through dashboarding tools like Tableau or Looker Studio Knowledge of data governance, security, and privacy practices Strong problem-solving, research, and troubleshooting skills for complex data issues Excellent communication and collaboration skills for cross-functional teams Ability to work in fast-paced environments, manage multiple priorities, and meet deadlines Experience with complex data sets and providing actionable insights Strategic thinking, multitasking, and a collaborative work approach Experience with coaching and mentoring team members Experience with API development for exposing ML/AI models (e.g., FastAPI) Experience working remotely (desirable) Interest or experience in behavior change theories (desirable) Full Data Science development lifecycle experience Experience with sensitive, healthcare-related data (desirable) Familiarity with Figma (optional) Responsibilities: Evaluate and implement new technologies in Data Science, Machine Learning, and AI. Contribute to the development and execution of the Data Science strategy. Provide evidence-based recommendations to guide strategic program implementation. Measure and analyze project impact to assess goal achievement. Drive data collection and refine existing data sources. Collaborate on MLOps best practices, including CI/CD, model monitoring, and production scaling with DataOps Engineering. Work with DataOps Engineering to build automated impact measurement frameworks (e.g., A/B testing, causal inference). Foster cross-disciplinary collaboration within the team to extract value from diverse skill sets. Collaborate with multidisciplinary teams to implement programs effectively. Partner with Monitoring and Evaluation, Strategy, Experience Design, Engineering, and SRE teams to develop monitoring tools for impact measurement. Define and maintain KPIs to track the success of data initiatives.

Location: Johannesburg, ZA

Posted Date: 1/17/2025