Our client is a top investment manager, globally, who is seeking a Senior Machine Learning Scientist for their Data Science group. The individual will contribute to ML strategy, lead teams of junior scientists and engineers for large initiatives, and drive research, hands-on, as a senior scientist. The Data Science group enjoys executive-level support, their mandate is broad, and they're already making a firm-wide impact. This is an excellent opportunity for an expert ML scientist to work with like-minded people on a broad set of interesting problems. Experience in finance/investment management is helpful but not at all required for this position.
- Advise group Director on research direction and ML strategy for the firm
- Lead research projects from idea generation and prototyping through validation with investment teams and deployment into production; this may involve project-based leadership over cross-funtional teams of junior scientists and other engineers
- Develop predictive models to forecast economic and business outcomes
- Architect ML-based optimizations to existing systems and proecesses within our data science stack
- Advise quantitative investment teams who are incorporating machine learning techniques into their research process
- Mentor junior scientists on machine learning and data science techniques
The ideal candidate has proven technical expertise and independence in commercial settings, and enjoys working hands-on in applied research. S/he is intellectually curious and a quick-study, but also self-directed and experienced in managing the demands on multiple projects at one time. The individual has passion for collaborating with like-minded individuals and an eagerness to apply themselves in the context of a collaborative, investment decision-making process.
- Advanced degree - most likely a PhD - in a quantitative field such as computer science, statistics, electrical and computer enginereing, etc.
- Strong background in classical machine learning, ideally including at least five yeras of relevant experience across academic and commercial settings. 3+ years of commercial experience is ideal.
- Deep experience working with probability, statistics, time-series and cross-sectional analysis, especially with very large data sets
- Fluency with one or more programming lanauges including Python, R, Scala, Julia, Java, C/C++, C#, etc.
- Interest in applying computational methods to tackle challenging real-world problems within domain such as economics and finance
Not required (but helpful):
- Familiarity with finance and portfolio management concepts
- Experience working with financial datasets
- Experience building deep learning models