Data Scientist

Data Scientist

Apply data mining techniques and conduct statistical analysis on extensive, structured, and unstructured datasets to understand and analyze phenomena. You will model business challenges, uncovering insights and opportunities through statistical, algorithmic, machine learning, and visualization methods. Collaborating closely with clients, data, and technology teams, you will transform raw data into critical information essential for informed business decision-making.

Experience Required

• Possess 3-4 years of experience in Data & Analytics / Data Monetisation.
• Have experience working with both structured and unstructured data.
• Demonstrate strong knowledge of data architecture, ETL processes, and data flows.
• Bring experience in data mining and statistical analysis.
• Exhibit proficiency in SAS, R, or Python, along with visualization tools such as Power BI or Tableau.
• Have experience with machine learning modeling and predictive analytics.
• Possess an understanding of banking data, systems, and products.
• Show exposure to software engineering, Business Intelligence (BI), and project delivery.

Essential Functions

• Develop and deploy machine learning and statistical models to address complex business problems.
• Perform data preprocessing, cleansing, and feature engineering to enhance model performance.
• Analyze large datasets to identify patterns, trends, and insights crucial for decision-making.
• Design and maintain data pipelines and ETL processes to support analytical initiatives.
• Translate business requirements into actionable data science solutions in collaboration with stakeholders.
• Build and maintain predictive models for areas such as credit risk, customer churn, and propensity.
• Present findings and insights through visualizations and reports to business stakeholders.
• Support the deployment and integration of models into production systems, ensuring optimal performance.

Additional Information

Behavioral Competencies

• Articulating Information
• Challenging Ideas
• Examining Information
• Interpreting Data
• Meeting Timescales
• Producing Output
• Providing Insights

Technical Competencies

• Data Analysis
• Database Administration
• Data Integrity Management
• Machine Learning & Modeling
• Knowledge Classification
• Research & Information Gathering