Data Scientist, Investment Data

• Develop and continuously improve practical methods for named entity recognition (NER), and create knowledge graphs at scale
• Create methods, algorithms & processes to identify, link, explore and assess billions of data points.
• Test structured and unstructured data sets for data quality and reliability.
• Collaborate with quantitative researchers – support alpha projects with your data expertise.
• Collaborate with technology specialists – help trade off application performance and operational feasibility.
• Support production deployments of your work to ensure long-term reliability
• Enable accessibility and clarity of your work through robust documentation and exposition.
• Present findings and recommendations to key stakeholders in a clear, concise, and compelling manner.
• Analytical Mindset – You think critically and creatively and are adept at balancing intuition with statistical validation.
• Data Science – You have practical experience building, validating, and enhancing performance of ML & AI models on large data sets.
• Technical Capability – You thrive working through challenging data problems that come with ambiguity and business trade-offs.
• Financial Knowledge – Desire to acquire the financial context needed to understand your stakeholders and make good data decisions.
• Self-Starter – You are motivated and willing to take personal accountability for quality and timeliness of work.
• Collaborative Approach – You thrive in a collaborative environment and value shared success. You proactively solicit and provide input and excel at communicating complex and technical concepts.
• Time Management – You are able to manage and prioritize multiple ongoing projects.
• Academic Training – At a minimum, an undergraduate degree in finance, mathematics, statistics, computer science, engineering, or related discipline. Completion of a relevant graduate degree is considered an asset.

#LI-HYBRID #LI-RC1