Program Optimization and Data Lead at Taimaka April, 2025

About the Job

Taimaka’s enrollment and treatment process for children with acute malnutrition is entirely digitized. Staff using our mobile phone application are guided step-by-step through the treatment process for each case they see, with that data then uploaded to our database to create a complete record of every touchpoint a child has with one of our providers. This, in theory, provides us with a fantastic amount of data to use to inform program design and execution. However, sometimes, we often find ourselves with too much data and not enough staff capacity to use it well.

The Program Optimization and Data Lead role is crucial for transforming Taimaka’s data into smarter, more effective programming. Your core responsibilities will be:

• Optimize and Enhance Digital Tools: Continuously improve our ODK application to ensure high-quality data collection that supports real-time decision-making and evolving program needs (this involves ‘low-code’ technical work, collaborating on complex programming). Modify clinical guidance provided through ODK (in collaboration with our program team) to provide the highest quality care possible.
• Drive Data-Driven Program Improvement: Develop and execute a strategy to unlock the potential of our data. This involves proactively identifying key questions, conducting analyses (using R/Python, SQL), creating insightful dashboards (e.g., Metabase), and building systems to embed data use into operational workflows for senior staff and field managers. Work with senior program leadership to identify problems and brainstorm solutions. Again, we want someone who is going to actively suggest fixes, not someone who merely hands over data and stops there.
• Lead and Develop the Data Team: Manage and build the capacity of our M&E and data staff (currently ~3 FTEs, expected to grow) to effectively support data quality, analysis, and insight generation.

Our current data team consists of ~3 FTEs:

• An M+E Supervisor who monitors form submissions and makes corrections when needed, provides data on a by-request basis to program management staff, and who runs tri-annual field patient screening efforts with temporary staff contracted for ~1 week per screening.
• A Data Entry Clerk who manually enters some paper forms filled by staff not equipped with smartphones (e.g., community mobilizers conducting at-home follow-up visits for patients).
• A few part-time Field Data Collectors who conduct backchecking visits of admitted patients to verify their anthropometrics.

We expect that this team will need to expand by another few staff members as our program grows, meaning you will need to carry out some recruitment and hiring as well.

We view this role as fitting into the later half of someone’s early career. Our priority is finding candidates who can work entrepreneurially – identifying key problems, work independently, and self-start – and think for themselves. We’re looking for scrappy innovators, so if you think you fit that, please apply even if you have to do some learning on the job.

Specific Responsibilities

Optimize and Enhance Digital Case Management Software – 20% of your time

• Implement updates to the Open Data Kit (ODK) forms Taimaka staff use to enroll patients and track their progress through the program (e.g., add a new biographical data collection question to the admission form) to improve user experience and care outcomes
• Execute more complex additions, like adding a warning to facility staff if a child has already been seen that week and is coming in for a second time (this requires integrating data from database queries into ODK attachments to provide real-time data back to the form)
• Make changes to these core ODK forms to ensure they remain in line with treatment protocols (e.g., changing recommended drug dosages in the section of the form that provides treatment guidance, based on changes in treatment protocols provided to you)
• Develop new ODK forms to meet program needs, such as digital attendance verification, supervision checklists, mapping new catchment areas, and replacing paper-based forms with digital versions.

Conduct Proactive Data Analysis for Program Strategy and Optimization – 30% of your time

• Proactively identify opportunities and initiate data analysis projects to answer critical strategic questions, evaluate program components, and guide key decisions (e.g., site selection, or identifying causes of programmatic challenges like data fabrication).

  • Brainstorm solutions to identified issues with senior program staff, work with program staff to implement these solutions.

• In some cases, plan and run field research efforts to gather more data to factor into decision-making than we usually collect.
• For data collection efforts, expect to delegate a lot of the data collection efforts (once you plan it) to your junior staff. For more complex data analysis, expect to undertake this yourself. These tasks will often require reviewing academic and grey literature, searching practitioner forums and guidelines, and organising discussions with other organisations.

Ensure High-Integrity Data Pipeline – 10% of your time

• Design and implement systems for your staff to routinely check data issues (like duplicate patient IDs, duplicate form submissions, etc.). Monitor your staff’s performance in carrying out these checks.
• Take initiative to improve these quality checks without external guidance. Brainstorm and refine over time what data needs to be checked to prevent problems.

Proactively Mitigate Program Fraud Risks – 20% of your time

• Conduct proactive risk assessments for ways fraud could be committed by Taimaka staff or patient caregivers that would divert resources away from treatment. Prioritize the most impactful risks.
• Design and implement data-driven fraud prevention and detection measures – like randomized home visit checks, biometric verification, or other novel solutions – to prevent these risks. Refine these checks over time with minimal external guidance.

  • Implementation may include hiring and onboarding new members of the data team.

• Integrate fraud detection mechanisms into ODK forms, database systems, and M+E dashboards. For more complex prevention mechanisms requiring more in-depth coding abilities (like biometrics), project manage volunteer software developers.

Develop and Implement Data Insight and Reporting Systems – 10% of your time

• Identify key metrics program personnel need to make decisions and track program quality, through a combination of independent thought and work with the program team.

  • Some examples would be things like: facility-by-facility reports of reasons for non-recovery of patients, staff-level caseload reports to monitor distribution of workload, or automatically updating dashboards of stock levels at different facilities.

• Create dashboards or other methods (or delegate the creation of these) to share these metrics on an ongoing basis, empowering program staff.
• Continuously refine these systems to tune them to make sure the right data is getting to program staff in a way that is helpful to them.

Program Management for Monitoring and Evaluation – 10% of your time

• Work with Taimaka’s executive director and nutrition program director to set quarterly priorities for the data team. Translate quarterly targets into monthly and weekly plans for the team.
• Provide day-to-day oversight of the data team (2-6 people), conduct performance reviews, provide training, and support team members’ professional development.
• Write job descriptions, advertise new roles, and assess applicants to hire new members of the data team as needed.
• Coordinate with external collaborators, like volunteer developers, data scientists, or researchers assisting on specific projects.

Future Growth Trajectories

Future growth trajectories for excelling hires could look like:

• Overseeing a growing team and budget as our program expands and our data team grows with it
• Several years down the line, helping set up new data teams as we expand to new states in Nigeria
• As part of your professional development, we could explore more technical routes like investing in coding training to do more in-depth work on our digital case management system

About You

• This role will likely suit an early to mid-career public health, data, or M+E specialist, or a very bright early to mid-career generalist capable of learning on the job. Our preference is for someone with a few years of work experience under their belt, but we may make exceptions for truly exceptional candidates.
• If you are not sure whether you’re the right fit for the position, err on the side of applying. Our initial application is designed to be fairly painless to complete and our priority is finding candidates with high overall potential, an ability to learn, and who align with our core philosophy of cost-effectiveness and innovation, rather than who check specific boxes.

Must Haves:

Candidates must have the following to qualify:

• Bachelor’s degree or higher
• Past experience with:

  • Data analysis in either R OR Python Pandas
  • SQL OR proven competence in any non-statistical programming language (taken as evidence of your ability to quickly learn SQL)

• An ability to learn new skills, particularly by diving in headfirst and learning by doing
• An ability to set your own priorities and independently solve problems

Nice to Haves:

The more of these that describe you, the better, but none are required. Even if none of these describe you, but you feel like you are talented and can learn, err on the side of applying.

Past experience with:

• Technical:

  • An XLSForm based data collection platform (e.g., Open Data Kit, KoboCollect, SurveyCTO)
  • Python (general use, not for data analysis)
  • Metabase or similar BI/dashboarding software
  • Geospatial data collection and mapping using ArcGIS, QGIS or similar software

• General:

  • Public health, humanitarian interventions, or biostatistics
  • Field experience in an LMIC, particularly if you were doing work related to data collection
  • Overseeing small teams (1-5 people)
  • Familiarity with GiveWell/Effective Altruist methods of evaluating cost-effectiveness

• A Master’s degree in public health, statistics, economics, or similar
• 2 years or more of work experience in data or M+E or 4 years or more of other work experience