About the position
The successful candidate will work on some of the most challenging data science problems in Responsible Investing. In this role, you will help design custom solutions to meet clients’ sustainability needs and develop an understanding of emerging ESG trends and global regulations/standards. You will work closely with cross-functional teams including the portfolio construction group, investment analytics, and the global client group, communicate effectively with stakeholders and present insights. As a key member of our dynamic team, you will also help nurture a culture of continuous learning and innovation, ensuring that we remain at the forefront of Responsible Investing. The ideal candidate is ambitious, determined, and self-motivated, and able to navigate a fast-paced environment.
Responsibilities
- Support the Director of Responsible Investing and members of the Responsible Investing team in aspects of research, oversight, and preparation of external communications, presentations and materials.
- Write clear, concise answers to clients’ and prospects’ questions, based on knowledge of Acadian’s processes and pertinent new research.
- Explore structured and unstructured datasets with a focus on data preparation, transformation, outlier detection, and feature engineering.
- Collaborate on the design of ESG constraints for client-driven investment solutions, help build predictive models and design interactive data applications.
Requirements
- Bachelor's degree in Quantitative Finance, Computer Science, Mathematics, Statistics, or a related STEM field.
- 2+ years of experience on the buy-side or sell-side.
- Strong communication skills with the ability to translate complex analytical insights for both technical and non-technical audiences.
- Well-organized and detail-oriented, with a methodical approach to work, strong multi-tasking skills, and the ability to collaborate across regional offices and time zones.
- Proficient in financial analysis, modelling, and statistics, with experience extracting insights from heterogeneous multi-dimensional datasets.
- Ability to apply machine learning techniques (e.g., scikit-learn, PyTorch, TensorFlow) and present complex data visually using Matplotlib, seaborn, or Streamlit.
- Fluent in Python, with experience working with data processing libraries such as Pandas.
- Strong SQL skills, a good understanding of Linux, parallel computing tools, and experience with Git, Jira, and Confluence.
- A demonstrated interest in sustainability, systematic investing and a willingness to undertake self-study towards the CFA Sustainable Investing Certificate.
Benefits
- Casual office environment
- Top-notch benefits
- Excellent professional and personal development opportunities
