Market Data Risk Analyst

As a Market Data Risk Analyst at Natixis, your primary responsibilities include supervising the Market Risk and P&L indicators of Global Markets, managing daily production of market data across asset classes, and developing control frameworks for data certification. You will also collaborate with multiple departments, helping to meet clients' needs and produce presentations for internal committees.

To apply for the Market Data Risk Analyst position at Natixis, candidates are expected to have an advanced degree in computer science, data science, or finance. Proficiency in programming languages such as Python, SQL, and Java is essential, along with knowledge of financial products and risk factors. A minimum of 5 years' experience in financial services can be beneficial as well.

The ideal Market Data Risk Analyst at Natixis should possess strong programming skills in Python, SQL, and Java, along with proficiency in data manipulation and visualization libraries. Knowledge of APIs and an understanding of risk factors associated with interest rates, credit, FX, and equity is also crucial.

In the role of Market Data Risk Analyst at Natixis, you will engage closely with Front Office, FO Quants, and various risk quantitative methodology teams. This role necessitates strong communication and collaboration skills to effectively convey complex technical concepts to non-technical stakeholders.

The work culture for a Market Data Risk Analyst at Natixis is vibrant and inclusive, emphasizing collaboration and team bonding. Employees enjoy flexible work arrangements, opportunities for skill development, and engaging social activities that foster connection among colleagues, all while being part of a leading financial services organization.

To effectively manage market data controls, I rely on established frameworks that focus on both quantitative and qualitative assessments. I ensure regular updates and thorough checks across equity, FX, and rates to maintain the integrity of the data while collaborating with the relevant teams for insights.

In my previous roles, I have extensively used Python and SQL for data manipulation and analysis, helping streamline processes and improve accuracy in reporting. I also have experience using Java for developing applications which assist in data certification tasks.

When presenting to senior management, I begin with a clear agenda, ensuring to highlight the key findings succinctly. I use visual aids to make complex data more digestible and always prepare to answer questions, demonstrating my confidence in the material.

In a previous position, I identified inefficiencies in the daily data collection process. By implementing a new automated system, I reduced manual input time by 30%, improved accuracy, and ensured timely delivery of data to stakeholders.

When evaluating external market data providers, I focus on data accuracy, reliability, and the provider’s ability to meet our specific needs. I also assess their compliance with industry standards and seek feedback from colleagues who have utilized their services.

I keep updated by regularly reading industry publications, attending webinars, and being active in professional networks. I also participate in training sessions offered by my employer to ensure my knowledge remains relevant.

Teamwork is crucial in my role as a Market Data Risk Analyst. Collaborating with colleagues from different departments enables us to share knowledge, tackle complex challenges effectively, and provide a more comprehensive service to our clients.

In case of a disagreement, I would initiate a discussion to understand their perspective fully. I believe in addressing concerns by relying on data and objective analysis to find common ground, aiming for a resolution that maintains data integrity.

I am motivated by the constant evolution and complexity within the financial services industry. The opportunity to leverage technology to improve market data processes and contribute to meaningful decision-making is a key driving force for me.

I use a combination of tools like Matplotlib and Tableau for data visualization. Creating interactive dashboards that allow stakeholders to easily understand the data's significance is crucial in facilitating informed decision-making.