Data Scientist at Kifiya Financial Technology

What You’ll Do

• Model Development: Build, test, and validate predictive models for credit risk, customer analytics, and efficiency.
• Data Preparation: Collaborate with Data Engineering to clean, structure, and transform raw data into usable datasets.
• Feature Engineering: Design new features to improve model accuracy and business impact.
• Analysis & Insights: Conduct exploratory data analysis to uncover risks, opportunities, and trends.
• Collaboration: Work with cross-functional teams to integrate models into production and decision-making.
• Documentation: Maintain records of methodologies, experiments, and results for transparency and reproducibility.
• Continuous Learning: Stay current with new ML methods, tools, and best practices.

What You’ll Bring

• Bachelor’s or Master’s in Data Science, Statistics, Mathematics, Computer Science, or related field.
• 2–4 years’ experience in data science or advanced analytics.
• Proficiency in Python or R, with libraries such as pandas, scikit-learn, TensorFlow.
• SQL expertise and experience working with structured/unstructured data.
• Solid grounding in statistical modeling, hypothesis testing, and machine learning.
• Strong communication skills for both technical and non-technical stakeholders.
• Familiarity with Agile methods and tools (Jira, Confluence).

Preferred:

• Experience in financial services, fintech, or credit risk modeling.
• Knowledge of MLOps frameworks and model deployment practices.
• Exposure to cloud platforms (AWS, Azure, GCP) and big data tools (Spark, Databricks, Hadoop).
• Experience with visualization tools (Power BI, Tableau, etc.).