Requirements •        Bachelor’s degree in Actuarial Science, Computer Science, Data Science, Engineering, Statistics, Economics, Mathematics, Business or Physics

•        Master’s degree and professional qualifications will be added advantage.

•        Qualified or Nearly Qualified CFA will be added advantage

•        1-3 years relevant experience in Financial Services

•        Prior experience working in alpha capture, performance attribution or trading / decision analytics role will be added advantage

•        Strong analytical/modelling skills and business orientation with proven ability to use data and analytics to drive business results; strong technical background

•        Demonstrate solid experience working within a data team, including Time series analysis and modelling; Training and fine-tuning of the Machine Learning model for investment models; Strong knowledge of Python for data scientists (e.g., pandas), traditional Machine Learning and deep learning libraries (e.g., scikit learn, xgboost, TensorFlow, Torch, etc.); Data manipulation languages (e.g., SQL); Data visualization / presentation skills (e.g., Tableau, PowerBI and DOMO)

•        Demonstrate experience working with engineering, developers and other technology teams - Writing production quality code, unit testing and familiarity with version control; Familiar with cloud-based technologies.

•        Strong communications skills (both verbal and written) and the ability to present findings to a non-technical audience

Passion for learning and adopting a wide range of techniques in an agile environment

•        Work with portfolio managers to understand sources of alpha and opportunities to improve decision-making process

•        Build tools and systems to understand decision data, context and events around it to enhance ICEA LION Asset Management’s decision attribution capabilities and systematically identify opportunities

•        Work with partners in technology and user experience to build out tools providing real-time insights to portfolio managers and their teams

•        Take part in projects along the full data science spectrum. From data acquisition and wrangling, to model selection to presentation and data visualization.

•        Analyze and visualize diverse sources of data, interpret results in a business context and report results clearly and concisely

•        Work collaboratively with different business partners and be able to present results in a clear and concise manner

•        Assist with developing and deploying educational workshops/seminar series for staff to accelerate data maturity.

•        Communicate results/findings; draft and edit scientific abstracts, presentations, and journal articles.

       •        Present work at workshops, seminars, and conference proceedings within and outside of the company.