Index Creation for Organization Benchmarking
Budget: £750 – £1,500 GBP
Purpose
Build an index to assess and benchmark organizations using a values-based, systems-thinking approach across four core themes. I'm looking for a data analyst / scientist to help build the tools and dashboards to create the index.
Scoring System
Each pillar includes quantitative and qualitative indicators, aggregated into a weighted score:
- Total Index Score (out of 100)
- Scores per Pillar (e.g., Governance = 25 pts)
- Optional: Maturity Level Classification (Emergent, Developing, Leading)
Organizations are assessed through:
- Publicly available data (disclosures, ESG/CSR/HR reports)
- Structured survey submissions (optional for higher accuracy)
- Analyst reviews using a standardized scoring rubric
Sector & Market Benchmarking
Companies are grouped for comparative insight by:
- Market: UK, US, EU stock exchanges
- Sector: e.g., Tech, Finance, Retail, Public Sector
- Size: Large-cap, mid-cap, small-cap (if relevant)
This allows both cross-market and intra-industry insights, showing who's setting the standard—and who’s falling behind.
Experience with the following is required.
- Scrapy or BeautifulSoup (Python) for crawling and scraping
- Selenium for dynamic content (e.g. JavaScript-heavy sites)
- spaCy or transformers for NLP tasks
Build a scoring mechanism using a rule-based or ML model.
As more data comes in can we train a classification model to predict maturity levels based on text patterns.
Possible tools:
- Diffbot or SerpApi: For structured web data extraction
- MonkeyLearn or Zyte: For low-code NLP pipelines
- Google Cloud NLP or Azure AI Language: For scalable entity and sentiment analysis
- Airtable or Notion API: For lightweight data dashboards
Build an index to assess and benchmark organizations using a values-based, systems-thinking approach across four core themes. I'm looking for a data analyst / scientist to help build the tools and dashboards to create the index.
Scoring System
Each pillar includes quantitative and qualitative indicators, aggregated into a weighted score:
- Total Index Score (out of 100)
- Scores per Pillar (e.g., Governance = 25 pts)
- Optional: Maturity Level Classification (Emergent, Developing, Leading)
Organizations are assessed through:
- Publicly available data (disclosures, ESG/CSR/HR reports)
- Structured survey submissions (optional for higher accuracy)
- Analyst reviews using a standardized scoring rubric
Sector & Market Benchmarking
Companies are grouped for comparative insight by:
- Market: UK, US, EU stock exchanges
- Sector: e.g., Tech, Finance, Retail, Public Sector
- Size: Large-cap, mid-cap, small-cap (if relevant)
This allows both cross-market and intra-industry insights, showing who's setting the standard—and who’s falling behind.
Experience with the following is required.
- Scrapy or BeautifulSoup (Python) for crawling and scraping
- Selenium for dynamic content (e.g. JavaScript-heavy sites)
- spaCy or transformers for NLP tasks
Build a scoring mechanism using a rule-based or ML model.
As more data comes in can we train a classification model to predict maturity levels based on text patterns.
Possible tools:
- Diffbot or SerpApi: For structured web data extraction
- MonkeyLearn or Zyte: For low-code NLP pipelines
- Google Cloud NLP or Azure AI Language: For scalable entity and sentiment analysis
- Airtable or Notion API: For lightweight data dashboards