Data Scientist

QIT Software is looking for a Data Scientist for Luxury Real Estate Website. ( PART TIME)


Requirements:

- Strong experience in data analysis, research, or journalism — ideally within real estate, property, or luxury consumer markets
- Strong source-evaluation instincts: you can tell a credible, citable figure from an unverified claim and know when a second source is required
- Proficiency working inside CMS platforms (WordPress experience a plus) and comfort with AI-assisted content tools
- Excellent written English — able to turn raw data into clean, concise market commentary for a professional readership
- High attention to detail: you catch a figure that's been rounded differently across two sources, and you follow it up
- Self-directed and organised — capable of managing a rolling workload without close supervision, meeting regular weekly and quarterly deadlines
- Familiarity with the branded residences sector, luxury hospitality, or international property markets is a strong advantage
- Experience working with structured data from multiple formats (PDFs, web articles, press releases) preferred

Responsibilities:

- Review and audit existing market pages to ensure every data point is accurate, current, and properly sourced
- Cross-reference figures against primary sources — market reports, press releases, and news articles — flagging any that lack credible attribution
- Update market data using the internal AI builder and WordPress CMS on a rolling basis as new information becomes available
- Monitor inbound press releases and scraped news content, extracting relevant market figures and tagging them for ingestion
- Produce weekly market insight summaries highlighting notable data changes, emerging trends, and pipeline activity in the branded residences sector
- Compile quarterly reports synthesising market performance across key geographies, brands, and project types
- Maintain a source log that documents the origin, date, and confidence level of every figure on the platform
- Proactively identify gaps in coverage and surface recommendations for new data categories or geographies to track