Data Scientist
hace 3 semanas
We are a leading provider of big data analytics solutions for the finance industry. Our team is dedicated to empowering clients to enhance returns, reduce risk, and increase efficiency by integrating public information into their models and workflows.
As a Data Scientist at RavenPack, you will play a pivotal role in supporting the validation of new data products and LLM-driven agents, while contributing to the productization of innovative factors and workflows.
Your Responsibilities:- Collaborate with a diverse team of data scientists and engineers to develop and refine innovative data products.
- Design and implement data pipelines that facilitate the extraction, transformation, and loading of structured and unstructured data for analysis and modeling.
- Validate and assess the outputs from the Data Science Research Team, ensuring that the data products are robust, accurate, and aligned with business objectives.
- Conduct data quality checks and implement validation processes to maintain the integrity of data throughout the product life-cycle.
- Share insights and findings with both technical and non-technical stakeholders, contributing to a culture of knowledge sharing and continuous improvement.
- Bachelor/MSc in a quantitative field such as Data Science, Computer Science, Engineering, Applied Mathematics, or a related discipline.
- Strong analytical, quantitative, and problem-solving skills, with a proven track record of applying these skills in real-world scenarios.
- Proficiency in programming languages such as Python, with experience in developing data processing pipelines and utilizing libraries for data analysis.
- Familiarity with SQL and experience working with relational databases, knowledge of NoSQL databases is a plus.
- Experience in implementing data quality checks and validation processes, ensuring data integrity and reliability.
- Familiarity with cloud platforms (e.g., AWS) and experience with data engineering tools and technologies.
- Practical experience with Natural Language Processing (NLP) and large language models (LLMs).
- Exposure to big data technologies and frameworks, as well as familiarity with machine learning techniques and agentic workflows.
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