This work will require a broad range of internal and external collaborations, and working with the Malaria PST, you will interface with the internal Institute for Disease Modeling, the broader malaria modeling community, country programs and foundation country offices, and internal foundation functional teams, such as the Integrated Portfolio Management team.
Make and manage investments in malaria quantitative sciences teams around the world.
Inform the foundation's malaria eradication strategy, including product R&D and target product profiles (TPPs).
Support malaria product R&D efforts with mathematical modeling and quantitative analytics of interventions between and within product classes.
Facilitate product development stage-gate decisions, prepare stage gate dossiers, and provide thought partnership and analysis to internal product development experts and grantees.
Utilize your technical expertise and the talents of the malaria quantitative sciences community to address the most impactful issues.
Develop and use innovative quantitative approaches to explore scenarios for the optimal delivery of malaria interventions
Approach problem-solving using innovative framing and cross-disciplinary approaches to simplify your work and focus on the most essential issues
Work closely with internal and external partners to understand and prioritize current policy and programmatic problems and questions.
Clearly communicate sophisticated methods and results to diverse audiences.
Identify knowledge or data gaps in settings in which we work and propose solutions to strengthen the analytical tools and methods used by the malaria community, advancing best practices in packaging and disseminating data and insights.
Support an inclusive team and community culture through modeling foundation values, escalating issues in a timely fashion to appropriate stakeholders, and working with the Malaria PST and the internal Institute for Disease Modeling.
Your Experience :
We are looking for team members who enjoy working on complex problems and collaboratively designing and conducting analytics that inform the strategies and investments we make in approaches that transform lives of people around the world. Candidates must be excellent team players who are excited to work across the spectrum of malaria product development and delivery.
Master's degree required, preferably in a quantitative field (e.g., Statistics/Biostatistics, Applied Mathematics, Economics, Epidemiology, Computational Biology), PhD in a quantitative field is a plus.
Demonstrated ability to work collaborative and efficiently as part of a team.
Demonstrated experience in modeling (e.g., stochastic processes, compartmental models, or agent-based models) applied to infectious disease or other health topics, coupled with strong data analysis skills.
Experience in statistical inference preferred (e.g., maximum likelihood, Bayesian statistics, optimization).
Proficiency in at least one scripting language (e.g. R, Python, etc...)
Knowledge of disease control and public health issues in LMICs a plus; knowledge of malaria modeling and eradication principles desired.
Excellent communication skills required to ensure product profiles and delivery settings we work in can be parameterized into disease models, and outputs can feedback to product developers and decision makers.
Knowledge of the product development process and the importance of Target Product Profiles desired.
Demonstrated ability to work in a self-guided manner, in highly unstructured and ambiguous situations, and can multitask across priorities.
Ability to question and challenge colleagues including managers and partners in constructive manner.
Other Attributes :
Commitment to our core values, mission, and programs with an approach that is consistent with the foundation’s guiding principles and holding self to the highest ethical standards.
Self-starter with a bias for action, thought partnership, efficiency, humility, intellectual curiosity, and investing in relationships.
Excellent listening skills and empathy
Ability to learn new content quickly, identify connections and second-order implications, and establish credibility with internal/external teams.
