Data Scientist

Job Overview

As part of our Data Science team, you will work closely with cross-functional teams, including DataOps Engineering, to extract meaningful insights from large and complex datasets, develop visualizations to communicate these insights, provide data-driven recommendations to measure impact and improve services, and contribute to new machine learning and AI initiatives. Analyses and tools developed will be tied to measurable organizational outcomes and impact on health and policy challenges, including those faced by South Africa and the Global South. The suitable candidate would ideally have an interest in developing (or continuing to develop) expertise in combining digital technologies with evidence-based large-scale behaviour change.

Key Focus Areas

• Advanced analytics
  • Use SQL to enable and action dashboard development and ad-hoc reporting requests.
  • Use Python, Machine Learning, and AI to extract other insights or to build new chatbot features.
  • Review chatbots to ensure data gets captured as needed.
  • Use Tableau and Looker Studio to develop dashboards.
  • Collaborate with DataOps Engineering on automating recurring analysis and reporting workflows to improve efficiency.
  • Develop predictive and prescriptive analytics models to forecast health or behavioral outcomes.
  • Apply a range of Machine Learning and AI techniques, including supervised, unsupervised, and reinforcement learning, while optimizing workflows with a focus on key performance metrics like accuracy, precision, and recall.
  • Experiment with, implement and evaluate generative AI methodologies, such as Large Language Models (LLMs), transformers, and RAG techniques.

• Cross-disciplinary collaboration
  • Bridge the gap between Data Science and other teams to maximize the utility of our diverse skill sets.
  • Engage with internal stakeholders, such as funders or research partners, to align insights with strategic goals.
  • Work closely with DataOps Engineering to align data pipelines, MLOps infrastructure, and ETL processes with data science workflows.

• Team growth and self-growth
  • Play an active role in contributing to, and improving, the Data Science team's ways of work, and roadmap for the future.
  • Keep up to date with emerging technologies in the Data Science, Machine Learning, and AI space.

• Communication
  • Effectively communicate complex technical findings to non-technical stakeholders.
  • Communicate and collaborate with external partners when required.
  • Foster a data-driven culture by organizing regular knowledge-sharing sessions or workshops.
  • Facilitate cross-functional problem-solving sessions to align Data Science objectives with organizational needs.

• Ethical considerations
  • Integrate ethical considerations into data science practices, ensuring responsible and unbiased use of secure data.
  • Proactively address fairness, bias mitigation, and explainability in models deployed in sensitive domains like healthcare.

• Agile methodology
  • Embrace and contribute to an agile work environment, adapting quickly to changing priorities and requirements.

Responsibilities and Duties

• Evaluate new technologies for Data Science, Machine Learning, and AI.
• Actively assist with driving the Data Science strategy within the organisation.
• Provide evidence-based recommendations to inform the strategic implementation of programmes.
• Help us understand if our projects are achieving their goals by measuring and analysing their impact.
• Drive the collection of new data and the refinement of existing data sources.
• Collaborate on implementing best practices for MLOps, including CI/CD pipelines, model monitoring, and scaling production systems, in conjunction with DataOps Engineering.
• Collaborate with DataOps Engineering on building automated impact measurement frameworks, such as A/B testing or causal inference designs.
• Cross-Disciplinary interfacing: The Reach team is drawn from many different backgrounds. A focus area is understanding and extracting the value from these diverse skill sets and ensuring good collaboration with the Data Science team.
• Work collaboratively within the multidisciplinary teams implementing our programmes.
• Work closely with the Monitoring and Evaluation, Strategy and Experience Design, Engineering and SRE teams to design and develop monitoring tools for impact measurement.
• Define and maintain key performance indicators (KPIs) to track the success of data initiatives.

Qualifications

• A degree in Computer Science or a related field. A Master’s Degree in a related technical field (Data Science, Statistics, Math, Software Engineering, Computer Science) is preferred but not required.
• Mathematical training to graduate level (i.e., undergraduate degree in a quantitative subject).

Skills and Experience Required

• 3+ years experience working as a Data Scientist or in a similar role, with a strong focus on data analysis, data visualization, and Machine Learning and AI concepts.
• Proficient in programming languages such as Python, with a deep understanding of the principles of object-oriented programming.
• 3+ years of experience developing dashboards on Tableau, PowerBI, or similar.
• 3+ years experience with SQL.
• Strong experience with cloud platforms such as AWS, Azure, or GCP, and expertise in utilizing cloud-based data services (e.g., AWS Athena, AWS Redshift, Google BigQuery).
• Solid understanding of data modelling.
• Experience using version control systems (e.g., Git).
• Experience in delivering Data Science, Machine Learning, and AI projects to production environments using an agile and responsive approach.
• Ability to visually describe insights and data effectively using dashboarding tools like Tableau or Looker Studio.
• Familiarity with Figma for wireframing and design collaboration will be beneficial but not required.
• Familiarity with data governance, data security, and data privacy practices.
• Excellent problem-solving and research skills and the ability to troubleshoot complex data-related issues.
• Strong communication and collaboration skills to work effectively with cross-functional teams and stakeholders.
• Proven ability to work in a fast-paced environment, handle multiple priorities, and deliver high-quality results within deadlines.
• Experience solving difficult problems with poorly defined constraints.
• Proven ability to understand, prepare, and analyze large and complex data sets. Ability to collaborate and work with cross-functional, multi-disciplinary teams.
• Analyze data, draw insights, and prepare reports in a cohesive, intuitive manner.
• The ability to think strategically, act quickly, multi-task and work collaboratively in an environment that values creativity and flexibility.
• Coaching and mentoring.
• Experience with API development for exposing ML and AI models (e.g., FastAPI).
• Desirable: experience working with remote teams.
• Interest or experience with behaviour change theories.
• Experience in the full Data Science development lifecycle.
• Experience working with sensitive, healthcare-related data.