Quantitative Equity Data Science
CCL ConfidentialVancouver, British Columbia, Canada · Posted 4 months agoDescription
- 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.
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