Quantitative Analyst
6 months ago
**Introduction**
- Momentum, a financial services provider since 1966, known for its entrepreneurial spirit and innovative culture is committed to wealth creation and preservation, insurance, and income protection for all our clients. We do this through our understanding of the retail insurance, savings, and investment markets in SA.**Role Purpose**
- The **Quantitative Analyst **will provide financial analysis within a specific business context, to provide inputs and insight into order to support business decision making.**Requirements**:
**Qualifications**:
- Bachelor’s degree in Actuarial, Finance, Statistics, Economics, or related field. Advanced degree (e.g. FRM/CFA/ CA) preferred.
**Experience**:
- 3+ years of experience in insurance, investments, data science, financial analysis, consulting, or a similar role
- Top level proficiency in modelling techniques, software, data storage and reporting platforms including but not limited to SQL, Microsoft Excel, VBA, Python, QlikView and PowerBI.
- Familiar with behavioral finance concepts.
- Excellent quantitative and analytical skills with a keen attention to detail.
- Ability to communicate complex quantitative concepts effectively to both technical and non-technical stakeholders.
- Proven ability to manage multiple projects simultaneously and meet very tight deadlines in a fast-paced environment.
- Skills in SQL and DCS would be advantageous
- Experience with scenario testing and remuneration modelling would be advantageous
**Duties & Responsibilities**
**Internal Processes: Sales and Service Experience:
- Analyse various sources of information and translate findings into insights that answer specific business questions.
- Develop comprehensive models and analytic tools to analyze various scenarios, including but not limited to commission linked remuneration and incentive structuring, budgeting, forecasting, valuation, and risk assessment.
- Collaborate with cross-functional teams to gather relevant data and assumptions for modelling purposes.
- Conduct thorough research and analysis to understand industry trends, market dynamics, and competitive landscapes.
- Validate and refine existing tools, models, and processes to ensure accuracy, consistency, reliability, and effectiveness.
- Prepare detailed reports and presentations summarizing key findings and recommendations derived from analysis to highlight insights and inform decision-making.
- Assist in the preparation of proposals, business plans, and presentations for management and other stakeholders.
- Develop proposals to incorporate data science and machine learning into the current environment.
- Collaborate with BI function to establish fit-for-purpose in-house data warehousing capabilities.
- Find opportunities to link finance and BI workflow and teams.
- Perform financial modelling to identify trends and patterns that highlight anomalies, identify relationships and tracking exceptions.
- Provide financial analysis in the form of descriptive analysis, trend analysis and projections.
- Establish a platform to enable the projection of key financial metrics and financial analysis.
- Design and develop solutions that answer specific business questions and capitalises on opportunities identified.
- Manage the implementation of relevant statutory reporting requirements within the area of expertise or specific business context.
- Assess the impact of remuneration changes on individuals and guidelines.
- Monitor modelled remuneration outcomes against actual experience.
- Scenario testing of based on the adoption of remuneration
- Convert business requirements for remuneration principles into a dynamic predictive model outcome
- Identify, understand, and interpret data structures across various databases within the business to facilitate data analysis and continuous monitoring activities
- Source data and complete data extracts across a variety of systems in order to gather data in the applicable format.
- Perform ETL (extract, transform and load) processes to assist with data mining exercises
- Contribute to data integrity and cleansing activities in order to increase the quality of data.
- **Client Advisor: Stakeholder Engagement**:
- Provide authoritative expertise and advice to clients and stakeholders.
- Build and maintain relationships with clients and internal and external stakeholders.
- Deliver on service level agreements made with clients and internal and external stakeholders in order to ensure that client expectations are managed
- Make recommendations to improve client service and fair treatment of clients within area of responsibility.
- Participate and contribute to a culture which builds rewarding relationships, facilitates feedback and provides exceptional client service
- **People: Team Collaboration and Personal Development:
- Develop and maintain productive and collaborative working relationships with peers and stakeholders.
- Positively influence and participate in change initia
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