Technician

• Perform routine inspections and maintenance of coldroom refrigeration systems.
• Ensure proper refrigerant levels, clean coils, and lubricate moving parts as necessary.
• Diagnose and troubleshoot coldroom system malfunctions, identifying causes of issues.
• Repair or replace faulty components, such as compressors, fans, motors, and sensors.
• Execute preventive maintenance schedules for coldroom systems to prevent breakdowns.
• Conduct regular servicing, filter replacements, and part inspections.
• Monitor energy consumption patterns of coldroom systems and identify opportunities for optimization.
• Implement energy-efficient practices to reduce operational costs.
• Adhere to safety standards, regulations, and guidelines while performing maintenance tasks.
• Maintain accurate records of maintenance activities, repairs, and preventive tasks.

Mechanical Technician ensuring cold-room refrigeration systems work efficiently

Mechanical Technician – ACs, Coldrooms

Mechanical Technician maintaining cold-room refrigeration systems for a fast-growing Nigerian conglomerate. Ensuring optimal performance, safety, and energy efficiency while adhering to regulations.

Associate

• Build and maintain strong relationships with ecosystem partners
• Source and onboard high-quality vendors across 12+ countries (labs, equipment providers, energy services, etc.)
• Conduct vetting and support approval workflows for clients and partners
• Identify ecosystem gaps and proactively source new providers
• Support day-to-day management of the Ecosystem Marketplace
• Monitor and manage account lifecycle stages: Sign-up → Pending → Approved → Active
• Ensure all users meet the vetting and approval standards
• Maintain structured records of all marketplace activity
• Build clear, intuitive dashboards and reporting systems
• Design structured, user-friendly interfaces within tools like Airtable

Technical Product Associate building and scaling marketplace platform for FHSA

Technical Product Associate

Technical Product Associate for FHSA focusing on building marketplace platforms and managing ecosystem partners with an emphasis on automation and data tracking.

General

• Support partner organizations and treatment providers to strengthen clinical quality assurance systems across supported treatment sites through regular review and analysis of clinical KPIs and performance data
• Identify treatment quality gaps and work with partners to design targeted improvement strategies at country level
• Monitor implementation of clinical quality improvement standards and track progress against performance indicators
• Support clinical case reviews, peer-learning exchanges, and continuous learning initiatives among providers
• Contribute to strengthening clinical data systems and ensuring data-informed decision-making
• Engage with Medical Champions and technical advisory groups to promote adherence to the Ponseti treatment standards and best practices
• Support the development and dissemination of clinical guidance, tools and quality improvement resources
• Support routine review of treatment quality indicators, identify performance gaps, and work with providers and partners to implement corrective actions and quality improvement plans
• Conduct detailed reviews of clinical performance data and treatment outcomes to identify trends, root causes of quality concerns, and opportunities for continuous improvement
• Support effective adoption and utilization of digital health and program management systems across partner organizations
• Monitor and validate data in platforms (e.g., CommCare, Salesforce), ensuring completeness, accuracy, and timeliness
• Track system usage and follow up with partners to improve engagement and compliance
• Promote data use through dashboards, performance reviews and reporting to strengthen program and clinical outcomes
• Generate analysis and insights to support quality improvement, operational planning, and program performance management
• Support reporting needs across partners, ministries of health, and internal stakeholders
• Conduct routine data quality reviews and validation exercises to identify discrepancies, strengthen data integrity, and improve confidence in clinical and program performance reporting
• Work with treatment providers and implementing partners to establish systems for ongoing data verification, correction, and continuous data quality improvement
• Develop and deliver training on both clinical practices and digital tools (e.g., CAST, Salesforce, data use)
• Strengthen the capacity of partner organizations to independently deliver training, mentorship, and supportive supervision
• Support onboarding of new treatment centers and healthcare providers
• Coordinate and support clinical mentorship and technical training initiatives
• Strengthen local trainer networks and contribute to long-term workforce development strategies
• Maintain and improve standardized training curricula and learning materials
• Provide ongoing mentorship and supportive supervision to treatment providers and trainers to strengthen clinical competence, adherence to Ponseti standards, and effective use of digital systems
• Support providers to interpret and use clinical and program data to improve service delivery, treatment outcomes, and program performance
• Provide technical guidance to partners on the use of clinical and digital systems
• Provide front-line technical support, troubleshoot issues, and escalate complex system challenges
• Manage and track support requests through a ticketing system (e.g., Jira)
• Support procurement, distribution, and management of devices used in program delivery
• Contribute to the development of user guides, job aids, and technical support resources
• Document lessons learned, best practices, and program outcomes for internal and external audiences
• Contribute to research, case studies, presentations, learning products, and technical publications
• Participate in product development cycles and recommend system enhancements
• Represent the organization in technical forums, trainings, and conferences as appropriate while promoting partner visibility and leadership
• Build strong relationships with MiracleFeet’s global Technical, Monitoring & Evaluation, and Digital Systems teams to facilitate learning, knowledge exchange, and adoption of best practices across countries
• Support collaboration with implementing partners, Medical Champions, healthcare providers, and relevant technical stakeholders to strengthen clinical quality, data quality, and program performance
• Provide technical assistance and support to partners across both clinical and digital domains
• Facilitate cross-team collaboration between clinical, program, and product teams

Technical Program Officer supporting healthcare providers to improve clubfoot treatment

Technical Program Officer, Clinical – Systems

Technical Program Officer supporting healthcare providers in Nigeria to strengthen clinical quality and data systems. Collaborating with global teams to implement quality improvement initiatives.

• Advanced ML-modeling and data-exploration: ensembles and AI-algorithms, AI-initiatives management, external AI-services integration. Focus on models and solutions for: credit, fraud, marketing, collection and contact strategies, text, speech and behavioral analytics, dynamic pricing and limits.
• Stakeholders' expectations management: communication with risk (portfolio) team, collection team, other business units on score-modelling and backlog prioritization, task clarification.
• DS-team management: recruitment, training, performance improvement, scrum-servicing, task-management. Improvement DS-team communication with consumers and business needs understanding.
• Environment, process and tools management: git, Jira board, Confluence content, Agile rituals.
• ML-data management: colabration with DWH-team; data-availability, reliability and quality assessment; new/existent data-sources integrations support and management, data-flow stability control, feature-store administation.
• ML-model lifecycle management: from business needs identification to "sell", deployment and production-test stage. ML-models stability monitoring and quality control, reassessment and proactive quality improvement (re-calibration/ re-building).
• Knowledge management: Maintain up-to-date project documentation, implement standards, control discipline and maintain actuality for confluence descriptions, feature-store meta-data, git documentation, internal experience sharing and handover, new methodologies and tools review and implementation.

Data Science Manager leading a high-performing DS team in banking sector

Team Lead, Decision Science

Data Science Manager leading a high-performing decision science team within the banking sector. Focus on advanced ML-modeling, scorecard development, and risk monitoring.

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