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Process Manager
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The candidate must possess in-depth functional knowledge of the process area and apply it to operational scenarios to provide effective solutions. He/she must be able to identify discrepancies and propose optimal solutions by using a logical, systematic, and sequential methodology. It is vital to be open-minded towards inputs and views from team members and to effectively lead, control, and motivate groups towards company objects. Additionally, he/she must be self-directed, proactive, and seize every opportunity to meet internal and external customer needs and achieve customer satisfaction by effectively auditing processes, implementing best practices and process improvements, and utilizing the frameworks and tools available. Goals and thoughts must be clearly and concisely articulated and conveyed, verbally and in writing, to clients, colleagues, subordinates and supervisors.
Role and responsibilities:
Leadership and Mentorship
- Team Leadership : Lead and mentor a team of Data Scientists and Analysts, guiding them in best practices, Advanced méthodologies, and carrer development.
- Project Management : Oversee multiple analytics projects, ensuring they are completed on time, within scope, and deliver impactful results.
- 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
- Stakeholder Engagement : Work closely with key account managers, data analysts, and other stakeholders to understand their needs and translate them into data-driven solutions.
- Communication of Insights : Present complex analytical findings clearly and actionably to non-technical stakeholders, helping guide strategic business decisions.
Advanced Data Analysis and Modeling
- Develop Predictive Models : Create and validate complex predictive models for risk assessment, portfolio optimization, fraud detection, and market forecasting.
- Quantitative Research : Conduct in-depth quantitative research to identify trends, patterns, and relationships within large financial datasets.
- Statistical Analysis : Apply advanced statistical techniques to assess investment performance, asset pricing, and financial risk.
Business Impact and ROI
- 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.
- Cost-Benefit Analysis : Perform cost-benefit analyses to prioritize analytics initiatives that offer the highest return on investment (ROI).
Algorithmic Trading and Automation
- Algorithm Development : Develop and refine trading algorithms that automate decision-making processes, leveraging machine learning and AI techniques.
- 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
- Advanced Statistical Techniques : Expertise in statistical methods such as regression analysis, time-series forecasting, hypothesis testing, and statistics.
- 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).
- Programming Languages : Strong coding skills in languages like Python, commonly used for data analysis, modeling, and automation.
- Data Management : Experience with big data technologies, and relational databases to handle and manipulate large datasets.
- Data Visualization : Proficiency in creating insightful visualizations that effectively communicate complex data findings to stakeholders.
- Cloud Computing : Familiarity with cloud platforms like AWS, Azure, or Google Cloud for deploying scalable data solutions.
- Quantitative Analysis : Deep understanding of quantitative finance, including concepts like pricing models, portfolio theory, and risk metrics.
- Algorithmic Trading : Experience in developing and back testing trading algorithms using quantitative models and data-driven strategies.
Requirements :
- A bachelor's degree in a related field, such as computer science, data science or statistics.
- Proven experience of 8+ years in programming languages, machine learning, data visualization and statistical analysis.
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