Data Scientist – Financial Technology

Company: Property crowdfunding & syndicate platform

Location: United Kingdom

About Us:

We are a pioneering entity in the property investment syndicate sector, striving to redefine the paradigms of real estate investment. We offer a platform that amalgamates state-of-the-art technology with profound financial acumen, allowing users to explore a plethora of property investment avenues. Our mission is to facilitate informed investment decisions, optimise investment returns, and assist users in wealth accumulation through real estate investments.
Position Overview:

The Data Scientist will be a linchpin in the evolution of our property investment syndicate app, utilising their proficiency in data science, finance, and quantitative analysis to develop innovative features and tools. This role is crucial in enhancing the app’s efficiency, user engagement, and community-building aspects, impacting the overall user experience and the company’s growth trajectory.

Key Responsibilities and Tasks:

1. Quantitative Modeling:
Develop and refine quantitative models to aid in investment decision-making.
Apply advanced statistical and machine learning techniques to analyse financial data.
Collaborate with other teams to integrate developed models into the app.

2. Algorithmic Trading Strategies:
Design, develop, and test algorithmic trading strategies specific to property investment.
Continuously optimise strategies to ensure maximum returns and efficiency.
Collaborate with the development team to integrate trading strategies into the platform.

3. Financial Risk Forecasting:
Develop and implement financial risk forecasting models like VAR and ES.
Conduct rigorous backtesting to validate the effectiveness of the developed models.
Provide insights and recommendations based on risk assessment results.

4. User Personalisation:
Develop algorithms for personalised investment recommendations and content.
Work closely with the development team to integrate personalisation features into the app.
Continuously refine algorithms based on user feedback and behaviour analysis.

5. Behavioural Finance Insights:
Analyse user behaviour using behavioural finance principles.
Develop strategies and interventions to help users make optimal investment decisions.
Collaborate with UX/UI designers to implement behavioural insights into the app’s design.

6. Data-Driven Insights:
Analyse user data, market trends, and financial data to generate actionable insights.
Present insights in a clear and understandable manner to inform strategic decisions and product improvements.
Work with different departments to implement data-driven recommendations.

7. Performance Monitoring:
Define and monitor key performance indicators (KPIs) related to user engagement and retention.
Analyse performance data to optimise user engagement strategies.
Provide regular reports on performance metrics to the management team.

8. Software Proficiency:
Utilise advanced data visualisation and analytics tools like Tableau and Qlik Sense for insightful data representation.
Employ data analytics platforms like RapidMiner, Alteryx, and Knime for predictive analysis, machine learning, and data blending.
Leverage Looker for exploring and sharing real-time business analytics.

9. Collaboration and Communication:
Work closely with other teams, including development, marketing, and finance, to ensure the seamless integration of data science components.
Communicate complex data findings and insights in a clear and effective manner to non-technical stakeholders.
Participate in regular meetings to discuss project progress, challenges, and future plans.

10. Continuous Learning and Development:
Stay abreast of the latest developments in data science, machine learning, and financial technology.
Continuously enhance skills through professional development opportunities.
Share knowledge and insights with team members to foster a collaborative learning environment.

Additional Tasks:
Participate in user acceptance testing (UAT) to ensure the functionality and user-friendliness of new features.
Address any issues identified during UAT and refine features as needed.
Assist in the documentation and user training processes for new features.

Qualifications:
Master’s or higher in Finance, Data Science, or related field.
Minimum 5 years of experience in data science, financial modelling, and quantitative analysis
Proficiency in Python and experience with data analysis and machine learning libraries
Comprehensive understanding of quantitative finance concepts and risk management.
Experience in algorithmic trading and financial risk modelling is essential.
Exceptional analytical, problem-solving, and communication skills.
Knowledge of real estate finance or property investment is advantageous.

Must-Have Experience and Knowledge in the Following Software:

Tableau: For advanced data visualisation and business intelligence.
RapidMiner: For advanced analytics including predictive analysis and machine learning.
Alteryx: For data blending and advanced data analytics.
Qlik Sense: For self-service data visualisation and analytics.
Knime: For creating data science applications and services.
Looker: For exploring and sharing real-time business analytics.

Benefits:
Competitive Salary and Bonus Structure.
Professional Development Opportunities.
Collaborative and Dynamic Remote Work Environment.
Opportunity to work in a rapidly growing sector with impactful projects.

How to Apply:
Interested candidates who are enthusiastic about leveraging data science and fintech to revolutionise property investment are encouraged to apply. Please respond to this post with links to your previous work; applications without this will not be considered.

We are an equal opportunity employer, valuing diversity, and are committed to creating an inclusive environment for all employees.