Data Analyst — Data MarketPlace
MSCI · London
The Data Analyst works hands-on with the datasets that feed the MSCI Data platform. The role has two primary purposes. First, to perform crude, first-pass analysis on datasets before integration — assessing their basic commercial value and quality readiness — so the team can decide quickly and confidently whether a dataset is worth onboarding. Second, to act as a quick-response team that creates new datasets from MSCI internal data: extracting value, aggregating and deriving novel datasets, and authoring the first working version of the methodology before partnering with Technology to scale and automate it. This is exploratory, judgement-led work that sits ahead of the formal data pipeline; production-grade validation and automation are owned by the QA and engineering teams in Technology, with whom the Analyst works closely.
Pre-integration value & quality assessment
- Perform crude, first-pass data analysis on external vendor datasets before integration, to assess their basic commercial value and quality readiness.
- Assess internal MSCI datasets for quality and readiness ahead of inclusion in the MarketPlace.
- Explore and profile datasets — coverage, frequency, history, schema and obvious quality issues — enough to support a fast onboarding decision.
- Document a concise value-and-quality readiness view per dataset, flagging the key strengths, gaps and risks.
- Validate the datasets already offered on the MarketPlace on an ongoing basis, confirming they remain accurate, current and as described.
Collaboration & promotion
- Work closely with the Data Scout to evaluate candidate external datasets early in the sourcing process, so commercial and quality views are joined up.
- Work with Applied Research to understand what makes a dataset valuable for backtesting and signal work, and feed that lens into assessments.
- Work with the Technology QA team, handing over assessed datasets and informing the automated validation checks they build and own.
- Contribute to the development and promotion of the MarketPlace by generating per-dataset analytics — summary statistics, coverage charts, illustrative use cases — for the marketing newsletter and the platform.
New dataset creation from internal data
- Act as a quick-response team to explore MSCI internal data and rapidly extract value where there is a clear product or client opportunity.
- Aggregate, combine and derive new datasets from existing internal data, prototyping the output before any heavy engineering investment.
- Author the first working version of the methodology to aggregate and construct each new dataset — the reference definition that Technology then productionises.
- Work with the Research team and the relevant product heads (Index, Analytics, S&C, Private Assets) to shape new datasets that have product relevance, settle the right methodology early, and ensure the output is useful to the teams who will build on it.
- Validate the prototype dataset and methodology, then hand over to Technology to scale, automate and operationalise.
- 3-5+ years in a data analyst, data science or quantitative data role, ideally within financial data or asset management.
- Strong Python and SQL skills; comfortable manipulating large, wide and messy datasets (e.g. pandas, relational databases).
- Demonstrated experience in data cleaning, validation and quality assurance.
- Strong exploratory data analysis skills — able to quickly make sense of an unfamiliar dataset and form a view on its value and quality.
- Comfortable using AI / LLM tools to speed up exploration and profiling (the production validation automation is owned by Technology QA, but familiarity with these tools is a plus).
- Solid grounding in financial data concepts — instruments, identifiers, corporate actions, time series and point-in-time data.
- Detail-oriented, with the judgement to spot when data ’looks wrong’ and the rigour to prove it.
- Familiarity with MSCI data (Barra factor / risk models, indexes, ESG & climate) or comparable provider datasets (preferred).
- Experience with cloud data platforms (e.g. Snowflake, Databricks, GCP) and data-pipeline tooling (preferred).
- Exposure to backtesting or signal research on financial datasets (preferred).