Member of Global Analytics, Data Science
About the Role
You will build and maintain the data foundation that supports trading and related functions. You will design and maintain scalable data pipelines, produce standardized datasets and metrics, and deliver analytics and dashboards for daily trading operations, PnL reporting, and liquidity monitoring. You will perform detailed reconciliations across disparate datasets, apply data modeling, lineage, and validation best practices, and use cloud-based tools and workflow management to automate processes. You will translate technical concepts into clear visuals and narratives, document data outputs, and partner with Finance, Operations, and Risk to reconcile differences and improve data quality.
Requirements
- Bachelor’s or advanced degree in Computer Science Statistics Information Systems Economics or related quantitative discipline; advanced degree preferred
- 2-4 years of experience in data science analytics or data management, ideally within financial services trading or fintech
- Experience with Snowflake BigQuery AWS or GCP
- Familiarity with Airflow dbt or similar scheduling and transformation tools
- Ability to build dashboards in Tableau Power BI Looker or equivalent
- Strong understanding of data modeling data lineage and validation
- Comfortable using Git and modern workflow management practices
- Direct experience performing detailed reconciliations with disparate datasets
- Familiarity with financial services regulatory reporting and compliance standards
- Strong analytical skills and ability to handle large datasets using statistical or econometric tools
- High attention to detail and cross-functional execution discipline
Responsibilities
- Design and maintain scalable data pipelines
- Develop standardized datasets and metrics
- Deliver analytics supporting trading operations and PnL reporting
- Monitor liquidity and produce related analytics
- Perform detailed reconciliations across disparate datasets
- Apply data modeling, lineage, and validation best practices
- Create clear dashboards and visualizations
- Document data outputs and provide visibility into data quality
- Partner with Finance Operations and Risk to ensure data consistency
- Automate data transformations and scheduling using modern workflow tools