Data Scientist- Associate

BlackRock
BU3-Budapest-GTC White House, Vaci ut 47, District XIII, BudapestPosted 2 April 2026

Job Description

About this role Introduction to GPS The Global Product Solutions (GPS) business at BlackRock aims to deliver innovative investment solutions and services that meet the evolving needs of our clients. Our mission is to provide top-tier financial products and advice to help clients achieve their financial goals. Your Team and Role We are looking for a Data Scientist to join our newly established Chief Data Office . Y ou will be responsible for working with our Global Business Intelligence (GBI) team to leverage data and analytics to provide actionable insights, drive strategic decisions, and enhance the overall performance of our products and services. You will collaborate with multidisciplinary teams to develop data-driven solutions that support our global operations. In addition to your technical skills, you will need to possess excellent people skills . You will interact with various stakeholders, including product managers, analysts, and senior leadership, requiring you to pitch your analysis and explain complex concepts to non-technical audiences. Your ability to communicate effectively and collaborate across different teams will be crucial in driving successful data-driven initiatives and ensuring the alignment of data strategies with business goals. Your Responsibilities Analyze complex datasets to extract meaningful insights and identify trends that impact business performance. Develop and implement machine learning models to optimize investment strategies and improve client outcomes. Create and maintain data pipelines to ensure the efficient processing and integration of large volumes of data. Collaborate with product managers, analysts, and other stakeholders to understand business requirements and translate them into data-driven solutions. Present findings and recommendations to senior leadership and other key stakeholders in a clear and concise manner. Stay updated on the latest advancements in data science and analytics to continuously enhance our methodologies and tools. Qualifications and Experience Bachelor's or Master’s degree in Data Science , Computer Science, Statistics, Mathematics, or a related field . Proven experience in data analysis, machine learning, and statistical modeling. Proficiency in programming languages such as Python, or R and SQL . A flexible analytic approach that allows for results at varying levels of precision Ability and interest in diving deep into complex data models and ETLs in order to come up with solutions to streamline, scale and automate existing or new processes Experience with data visualization tools like Tableau, Power BI, or similar. Strong critical thinking skills and the ability to work with large, complex datasets. Excellent verbal and written communication, teamwork, and relationship-building skills Preferred Skills Experience in the financial services industry or a related field. Understanding of investment products and financial markets. Our benefits To help you stay energized, engaged and inspired, we offer a wide range of employee benefits including: retirement investment and tools designed to help you in building a sound financial future; access to education reimbursement; comprehensive resources to support your physical health and emotional well-being; family support programs; and Flexible Time Off (FTO) so you can relax, recharge and be there for the people you care about. Our hybrid work model BlackRock’s hybrid work model is designed to enable a culture of collaboration and apprenticeship that enriches the experience of our employees, while supporting flexibility for all. Employees are currently required to work at least 4 days in the office per week, with the flexibility to work from home 1 day a week. Some business groups may require more time in the office due to their roles and responsibilities. We remain focused on increasing the impactful moments that arise when we work together in perso ... (truncated, view full listing at source)
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