Senior Data Scientist

The Data Scientist at Keytrade Bank plays a central role in transforming data into smarter decisions, AI solutions, and exceptional customer experiences. In this dynamic position, you'll design and train ML models, uncover valuable insights, and drive innovation by exploring new technologies, data sources, and methodologies. Working closely with product teams and stakeholders like marketing and operations, you'll help shape data-driven products from concept to execution while elevating the bank's analytical capabilities.

Specific competences needed:

• Proficiency in languages like Python, R, SQL, and Java.

• Database Management: Familiarity with databases like MySQL, PostgreSQL

• Big Data Technologies: Familiarity with platforms like Hadoop and Spark.

• Statistical Analysis: Proficiency in statistical hypothesis testing, regression analysis, and time series forecasting.

• Data Wrangling: Skills in data cleaning, transformation, and ETL processes.

• Predictive Modeling: Ability to develop models for credit scoring, email routing, fraud detection, customer segmentation, and other banking-specific use cases.

• Quantitative Analysis: Handling, analyzing, and interpreting complex quantitative data.

• Data Visualization: Proficiency in Power BI.

• Industry Knowledge: Understanding of banking operations, products, services, and regulations.

• Risk Management: Knowledge of risk assessment and the ability to integrate risk management into data analyses.

• Business Intelligence: Ability to transform data insights into actionable business strategies.

• Communication: Ability to present findings in a clear and compelling manner to non-technical stakeholders.

• Teamwork: Collaborate effectively with different departments like IT, marketing, and finance.

• Problem-solving: Innovative thinking to approach complex data problems.

• Continuous Learning: The field of data science is rapidly evolving; a commitment to continuous learning is crucial.

• Ethical Judgement: Recognize the ethical considerations related to data privacy, fairness, and biases in modeling.

• Regulatory Compliance: Understanding of financial regulations like Basel III, GDPR, CCPA, and others that affect data use and analytics.

• Fraud Detection: Knowledge of specific methods and tools for detecting financial fraud.

• Customer Analytics: Insights into customer behavior, segmenation, lifetime value, and churn prediction.

• Version Control: Familiarity with tools like Git for managing code and projects.

• Data Warehousing Solutions: Knowledge of tools like Wherescape, Snowflake, Redshift, or BigQuery.

Do you have experience in Spark?, How many years of experience do you have with the following?

• Python, R, SQL, and Java

• Database Management (e.g., MySQL, PostgreSQL)

• Big Data technologies such as Hadoop and Spark