Data Scientist

Hybrid - London

FactSet is a global leader in providing cutting-edge research and analytical tools to finance professionals. We offer instant access to accurate and comprehensive financial data and analytics worldwide. FactSet clients integrate hundreds of databases from industry-leading suppliers into a single, powerful information system.

About Enterprise Analytics: The Enterprise Analytics group at FactSet focuses on internal information to support product development and internal sales teams. Our work spans a diverse array of projects and teams, reflecting our broad scope and impact. We analyze user engagement patterns to identify trends and at-risk users, and recommend product bundling strategies. Our team processes and examines internal documents to uncover opportunities, and we are at the forefront of developing tools to work with data from LLM-powered tools. We collaborate closely with our stakeholders over extended periods, helping the business make informed, impactful decisions. Throughout these processes, we leverage both traditional and state-of-the-art machine learning and data analytics techniques, ensuring we remain at the cutting edge of the industry.

Responsibilities:

• Data Analysis and Hypothesis Testing: Conduct fundamental data analysis and hypothesis testing under the guidance of a senior data scientist. This includes running experiments, developing models, and interpreting outcomes. Generate valuable insights from data to inform business decisions and strategies, validating or rejecting hypotheses to enhance our understanding and drive further exploration.
• Data Cleaning and Preparation: Access and retrieve data from multiple sources, ensuring it is ready for comprehensive analysis. Clean the data by identifying, addressing, and resolving issues related to its quality and integrity.
• Learning and Skill Development: Engage in continuous learning opportunities such as formal training sessions, coaching, and self-study to build a robust foundation in data science principles. Consistently improve your skills, enhance productivity, and gradually take on more complex tasks as you grow in your role.

Requirements:

• Bachelor's degree or higher in computer science or science related field.
• Proficiency in programming in Python, experience in spark a plus.
• Knowledge of SQL for data extraction and manipulation.
• Familiarity with data visualization tools such as Tableau, Power BI, or similar.
• Understanding of machine learning algorithms and statistical methods.
• Experience with data cleaning, wrangling, and preparation techniques.