Data Analyst — Data MarketPlace

MSCI Inc; City of Westminster; On-site; GBP 45,000 - 70,000; Full time

Job summary

MSCI Inc. is seeking a Data Analyst to perform first-pass data analysis on external vendor datasets and internal MSCI data to assess value and quality before onboarding.

You will document concise readiness views for each dataset and collaborate with Data Scout, Applied Research, and Technology QA to shape scalable datasets for MarketPlace. This role involves pre-integration analytics, rapid dataset creation from internal data, and working with multiple teams to define and validate methodologies

Qualifications

• 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.
• 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).

Responsibilities

• 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 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.

Skills

Python

SQL

Pandas

Relational databases

Data cleaning

Data validation

Quality assurance

Exploratory Data Analysis

AI/LLM tools

Financial data concepts

Attention to detail

MSCI data familiarity

Snowflake

Databricks

GCP

Data pipelines

Backtesting

Job description

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).