Senior Data Scientist

**Project description**:
Our Client is the corporate and investment banking arm of the Group, world’s 10th largest bank by total assets.

Their Singapore center, Information Systems Asia Pacific (ISAP), is the 2nd largest IT setup (after Paris Head Office) for the Group's worldwide business. They work daily with international branches located in 30 markets by:

- Envisioning and preparing the Bank’s futures information systems
- Partnering and supporting core banking flagships and transverse areas in their large scale development projects.

This unique positioning empowers us to bring our core banking business a sustainable competitive advantage on the market.

We seek innovative and agile people sharing our mindset to support ambitious and forthcoming technological challenges.

We are seeking a talented Data Scientist to join our dynamic Capital Market IT department

As a Data Scientist, you will be responsible for participate in development, training, deployment and management of data quality monitoring models using ML and GenAI. The person should be an experienced data scientist and shall also have the mindset to keep improvement of the model development process. You will work autonomously and follow a continuous improvement approach, ensuring high-quality code that adheres to our design, norms, and standards. You will be accountable for delivering solutions that meet both functional and non-functional requirements, taking into account the principles of Agile development.

CMI involves technology solutions and systems used in financial markets for trading, investment, and related activities. This includes electronic trading platforms, risk management systems, market risk, counterparty risk, algorithmic trading, data analytics, and Regulatory measures. The use of advanced technologies like API’s, artificial intelligence and cloud solutions are also becoming increasingly prevalent in capital markets to enhance efficiency and decision-making processes.

**Responsibilities**:
Identify and create relevant features from transaction data to improve model performance.

Perform feature selection and dimensionality reduction to enhance model efficiency.

Develop, train, and evaluate machine learning models using transaction data.

Implement cross-validation and hyperparameter tuning to optimize model performance.

Monitor model performance over time to detect and address issues such as data drift and model degradation.

Implement model retraining and updating processes to maintain model accuracy and relevance.

Develop and execute strategies to integrate Generative AI tools and techniques into transaction data monitoring processes, improving efficiency and reducing development time.

Design test and refine prompts for LLM to improve accuracy, reliability and efficiency.

Identify opportunities to augment data science workflows using GenAI, enhancing model development, data analysis, and feature engineering capabilities.

Collaborate with data engineers, software developers, and business analysts to ensure seamless integration of data quality monitoring processes.

Work closely with domain experts to understand transaction data requirements and business rules.

Effectively communicate data quality findings, model performance, and project progress to stakeholders.

Provide actionable insights and recommendations to improve data quality and model performance.

Maintain comprehensive documentation of data quality monitoring processes, machine learning models, and generative AI models and processes.

Document data pre-processing steps, feature engineering techniques, and model evaluation results.

Stay updated with the latest advancements in machine learning, generative AI, and data quality monitoring techniques.

Experiment with new tools, technologies, and methodologies to enhance data quality monitoring and model performance.

**Skills**:
Must have

8+ years experience in relevant activities.

Proficiency in languages such as Python, R, SQL, and Java or Scala.

Strong understanding of statistical methods and concepts.

Experience with data manipulation libraries (e.g., pandas, dplyr) and data analysis tools.

Knowledge of machine learning algorithms and frameworks (e.g., Scikit-learn, TensorFlow, PyTorch).

Expertise in analyzing and forecasting time series data.

Ability to create visualizations using tools like Matplotlib, Seaborn, ggplot2, or Tableau.

Big Data Technologies: Familiarity with big data tools such as Hadoop, Spark, and distributed computing.

Database Management: Experience with relational databases (e.g., MySQL, PostgreSQL) and NoSQL databases (e.g., MongoDB, Cassandra).

Docker/Kubernetes, Kafka, Spark, MongoDB

Data Wrangling: Skills in cleaning, transforming, and preparing data for analysis.

Knowledge on implementing ML and GenAI solutions AWS (Bedrock, SageMaker, etc..)

Excellent communication and interpersonal skills to effectively collaborate with diverse teams.

Excellent problem-solving