337

Manager volunteer roles in hereford, powys

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Remote
Unpaid role, expenses not paid
Voluntary

Actively Interviewing

This organisation is scheduling interviews as applications come in. They're ready to hire as soon as they find the right person. Don't miss your opportunity, apply now!

Job description

About DMAFB

From One Act of Kindness to a Global Movement

A stranger's simple act of helping someone find a home sparked a journey that would become DMAFB. Founded on the principle of "I help you, you help me. Everybody happy" - we're building a digital wellbeing platform that transforms micro-acts of kindness into tools for connection, cultural exchange, and mental wellbeing improvement.

Our Mission: Prevent burnout, improve workplace morale, and make kindness a natural part of daily work life through science-driven, human-first interventions.

What we're building

We're developing an MVP platform that combines:

  • Predictive analytics for early detection of workplace wellbeing issues
  • Real-time monitoring and personalised intervention recommendations
  • Time-series tracking of individual and team wellness metrics
  • Pattern recognition to identify hidden risks before they become crises
  • A lightweight, intuitive interface that seamlessly integrates into daily workflows

This isn't just another wellness platform - it's a proactive system that shifts organisations from reactive surveys to meaningful, data-driven support.

What we need

We're seeking a volunteer Full Stack Developer with experience in:

Recommended Tech Stack

Backend

Primary Framework Options:

  • Python with Django/FastAPI - Ideal choice given the ML/AI requirements (predictive modeling, anomaly detection with auto-encoders)
  • Node.js with NestJS - Good for real-time data processing and high concurrency
  • Java/Kotlin with Spring Boot - Enterprise-grade with strong security

Recommendation: Python with FastAPI

  • Excellent for ML integration (scikit-learn, TensorFlow, PyTorch)
  • Fast performance with async capabilities
  • Native support for data science libraries
  • Easy API documentation with OpenAPI/Swagger

Database Architecture

Primary Database:

  • PostgreSQL - ACID compliance, excellent for complex queries and analytics
  • Supports time-series data for tracking trends over weeks/months/quarters

Additional Data Stores:

  • Redis - Caching layer for real-time scoring and dashboard performance
  • TimescaleDB (PostgreSQL extension) - Optimized for time-series wellbeing data
  • Elasticsearch - Fast searching through historical patterns and anomaly detection

Machine Learning & Analytics

  • Python ML Stack:
    • scikit-learn for predictive modeling
    • TensorFlow/PyTorch for auto-encoder neural networks (anomaly detection)
    • pandas/NumPy for data analysis
    • SciPy for statistical analysis

Real-Time Processing

  • Apache Kafka or RabbitMQ - Event streaming for survey responses and intervention triggers
  • Celery - Asynchronous task queue for scheduled interventions and alerts

Frontend

  • React or Vue.js - Interactive dashboards
  • D3.js or Chart.js - Data visualizations
  • Material-UI or Tailwind CSS - Component library

Security & Privacy (Critical for HR Data)

  • OAuth 2.0 / OpenID Connect - Authentication
  • Role-Based Access Control (RBAC) - Manager/employee/HR permissions
  • End-to-end encryption for sensitive employee data
  • Audit logging for compliance (GDPR, HIPAA if applicable)

Cloud Infrastructure

Recommended: AWS or Azure

  • Compute: ECS/EKS (AWS) or AKS (Azure) for containerized services
  • Storage: S3/Azure Blob for documents and resources
  • CDN: CloudFront/Azure CDN for fast resource delivery
  • Monitoring: CloudWatch/Azure Monitor + DataDog or New Relic

Architecture Pattern

Microservices Architecture:

  1. Survey Service - Data collection
  2. Analytics Service - Scoring and pattern recognition
  3. Intervention Service - Triggered actions
  4. Notification Service - Email/in-app alerts
  5. Dashboard Service - API for frontend
  6. ML Service - Predictive modeling

Key Technical Considerations

For the Item-Level Anomaly Detection:

  • Auto-encoder neural networks to detect unusual patterns
  • Real-time scoring with <100ms response time
  • Batch processing for trend analysis

For Predictive Modeling:

  • Time-series forecasting models (LSTM, Prophet)
  • 1-4 week prediction windows
  • Continuous model retraining with new data

For Privacy:

  • Data anonymization at the database level
  • Aggregation services that prevent de-anonymization
  • Separate data stores for identifiable vs. anonymous data

What you'll contribute to

  • Core platform architecture for our MVP launch
  • Integration points for predictive models and analytics
  • User-facing features that make wellbeing interventions effortless
  • A scalable foundation that can grow with our community

What you'll gain

Purpose-driven work - Help prevent burnout and improve lives globally
�� Ground-floor opportunity - Shape the technical foundation of a growing movement
�� Collaborative team - Work alongside ML specialists and wellness experts
�� Portfolio value - Real-world experience building AI-integrated healthcare tech
�� Potential equity - As we grow, early contributors will be considered for equity opportunities

Posted by
Do Me A Favour Buddy View profile Organisation type Non Charity Employer
Posted on: 01 October 2025
Closing date: 13 November 2025 at 09:01
Tags: Human Resources, IT, Digital, Mental Health, Recruitment, User Experience / UI / UX, Wellbeing

The client requests no contact from agencies or media sales.