What you will do!
- Develop a deep understanding of Klivvr’s financial data landscape and associated data sources to gain insights into performance and identify optimization opportunities.
- Identify, design, and develop data solutions that support strategic initiatives and operational improvements.
- Apply machine learning tools to select features, create and optimize classifiers, and develop predictive systems.
- Enhance data collection procedures to ensure comprehensive data for developing analytic systems.
- Utilize first- and third-party data sources to deepen understanding of drivers of growth, efficiency, and customer loyalty.
- Acquire, cleanse, validate, and integrate data from various sources for discovery and model development.
- Analyze large volumes of information to identify patterns and derive actionable solutions.
- Present results clearly and propose solutions and strategies to address business challenges.
- Develop and implement an A/B testing framework to evaluate model quality and effectiveness.
- Enable a self-serve data environment using data modeling and visualization tools such as Looker, while supporting ad-hoc analysis requests as needed.
- Oversee the development and maintenance of financial alerting systems to keep relevant teams informed of incidents requiring immediate attention.
- Monitor key performance indicators to track and report on financial performance, providing data-driven insights and recommendations for optimization.
About You
- A minimum of 5 years experience in a data science role, ideally within a fintech or financial services organization.
- A background in Computer Science, Analytics Engineering, Mathematics, Statistics, Economics, or another quantitative field.
- Proficiency with Python or similar programming languages and associated data science packages.
- Proficiency in SQL (We use Google BigQuery).
- Experience with dashboard visualization tools such as Looker, Google Data Studio, Tableau, or similar.
- A strong collaborator with excellent communication skills, effective in working with cross-functional teams and managing stakeholder expectations.
- Experience in measuring the impact of data initiatives and presenting findings with clear, actionable recommendations.
- Intellectually curious, creative, and diligent, with a passion for both the business and the data.