Data Scientist

Role and responsibilities:

Leadership and Mentorship 

1. Team Leadership :

   Lead and mentor a team of Data Scientists and Analysts, guiding them in best practices, Advanced méthodologies, and carrer development. 

2. Project Management

   : Oversee multiple analytics projects, ensuring they are completed on time, within scope, and deliver impactful results. 

3. Innovation and Continuous Learning

   : Stay at the forefront of industry trends, new technologies, and méthodologies, fostering a culture of innovation within the team.  

Collaboration with Cross-Functional Teams 

1. Stakeholder Engagement :

   Work closely with key account managers, data analysts, and other stakeholders to understand their needs and translate them into data-driven solutions. 

2. Communication of Insights

   : Present complex analytical findings clearly and actionably to non-technical stakeholders, helping guide strategic business decisions. 

Advanced Data Analysis and Modeling 

1. Develop Predictive Models

   : Create and validate complex predictive models for risk assessment, portfolio optimization, fraud detection, and market forecasting. 

2. Quantitative Research

   : Conduct in-depth quantitative research to identify trends, patterns, and relationships within large financial datasets. 

3. Statistical Analysis :

   Apply advanced statistical techniques to assess investment performance, asset pricing, and financial risk. 

Business Impact and ROI 

1. Performance Metrics :

   Define and track key performance indicators (KPIs) to measure the effectiveness of analytics solutions and their impact on the firm's financial performance. 

2. Cost-Benefit Analysis :

   Perform cost-benefit analyses to prioritize analytics initiatives that offer the highest return on investment (ROI). 

Algorithmic Trading and Automation 

1. Algorithm Development :

     Develop and refine trading algorithms that automate decision-making processes, leveraging machine learning and AI techniques. 

2. Back testing and Simulation :

   Conduct rigorous back testing and simulations of trading strategies to evaluate their performance under different market conditions. 

What we're looking for

1. Advanced Statistical Techniques :

   Expertise in statistical methods such as regression analysis, time-series forecasting, hypothesis testing, and statistics. 

2. Machine Learning and AI

   : Proficiency in machine learning algorithms and experience with AI techniques, particularly in the context of predictive modeling, anomaly detection, and natural language processing (NLP). 

3. Programming Languages :

   Strong coding skills in languages like Python, commonly used for data analysis, modeling, and automation. 

4. Data Management :

   Experience with big data technologies, and relational databases to handle and manipulate large datasets. 

5. Data Visualization :

   Proficiency in creating insightful visualizations that effectively communicate complex data findings to stakeholders. 

6. Cloud Computing

   : Familiarity with cloud platforms like AWS, Azure, or Google Cloud for deploying scalable data solutions. 

7. Quantitative Analysis :

   Deep understanding of quantitative finance, including concepts like pricing models, portfolio theory, and risk metrics. 

8. Algorithmic Trading :

   Experience in developing and back testing trading algorithms using quantitative models and data-driven strategies. 

Requirements :

1. A bachelor's degree in a related field, such as computer science, data science or statistics.