Data Analyst — Data MarketPlace

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.

• 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 Market Place.

• 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 Market Place on an ongoing basis, confirming they remain accurate, current and as described.

• 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 Market Place by generating per-dataset analytics - summary statistics, coverage charts, illustrative use cases - for the marketing newsletter and the platform.

• 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 product ionises.

• 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 ope rationalise.

• 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).