Associate Process Manager

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.

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).

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.

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.

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

Proven experience of 6+ years in programming languages, machine learning, data visualization and statistical analysis.