I am a mathematician employed as a data scientist at a fin-tech bank in London’s City. With seven years of experience in modeling and data engineering within the retail and commercial banking sector, I specialize in crea…
I am a mathematician employed as a data scientist at a fin-tech bank in London’s City. With seven years of experience in modeling and data engineering within the retail and commercial banking sector, I specialize in creating credit (acquisition and portfolio), fraud prevention, and marketing models. My expertise spans the entire development-to-production cycle, including the creation of proprietary frameworks. These frameworks enable me to produce numerous model iterations per build and monitor their performance, emphasizing optimal variable use. I have authored multiple model documents, articulating model benefits, addressing biases, and explaining non-linear relationships using tools like SHAP. In my role, I am proficient in SQL, SAS, UNIX-based systems, Git, Apache Airflow, Apache Kafka, Docker, Octopus Deploy, Jenkins, and Python. This includes various Python data processing libraries such as pandas, scikit-learn, NumPy, Keras, TensorFlow, along with familiarity in Java, Scala, PySpark, and AWS. I played a key role in developing a customized decision engine commissioned by my bank. I served as the intermediary between the contractors responsible for building the engine and the management and technical teams within the bank. My responsibilities included ensuring that the build met specified requirements and complied with security and regulatory standards. I am good at explaining technical concepts to non-technical stakeholders and work well both independently and as part of a team.
As a data scientist operating within the financial sector, my role encompasses several key responsibilities:
1. Data Exploration and Cleansing:
Initiate data analysis procedures, focusing on exploratory techniques to discern patterns and trends within financial datasets. Rigorously cleanse and preprocess data to ensure its accuracy and reliability.
2. Predictive Modeling Expertise:
Employ advanced statistical techniques and machine learning algorithms to construct predictive models relevant to financial contexts. These models are tailored to scenarios such as credit scoring, risk assessment, and market forecasting, leveraging historical data for decision support.
3. Risk Management:
Contribute to risk management efforts by developing models that identify and evaluate potential financial risks. Utilize statistical analyses and predictive modeling techniques to inform strategies aimed at risk mitigation.
4. Fraud Detection:
Lead initiatives in fraud detection by constructing and refining algorithms to identify and prevent fraudulent activities in areas such as credit card transactions and money laundering. Ensure models evolve to counter emerging fraud patterns.
5. Customer-Centric Insights:
Utilize segmentation techniques based on customer financial behavior and preferences to contribute to the development of personalized financial products and services. Enhance customer experience and satisfaction through data-driven insights.
6. Regulatory Compliance Authority:
Address the complexities of financial regulations by developing models and systems that ensure organizational compliance. Stay abreast of regulatory changes and modify models accordingly to guarantee adherence.
7. Portfolio Management:
Contribute to the optimization and management of investment portfolios by applying quantitative methods to assess performance and risk. Provide data-driven insights to inform strategic decisions in portfolio management.
8. Data Communication :
Emphasize effective communication by translating complex findings into clear and compelling data visualizations and reports. Collaborate with non-technical stakeholders, including teams in finance and IT.
9. Continuous Learning and Innovation:
Embrace a commitment to continuous learning and innovation within the dynamic realms of finance and data science. Adopt new methodologies and leverage cutting-edge technologies to address evolving financial challenges.
Creating credit risk models
Creating fraud models
Creating marketing models
Worked within unsecured lending
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