Software Engineer

As a Data Scientist at Convera, you will play a pivotal role in shaping the future of payment products. Leveraging your technical expertise, analytical acumen, and product intuition, you will work with one of the most extensive data sets available. You will collaborate with a diverse range of cross-functional partners, including Product, Engineering, Data Engineering, Marketing, Sales, and Operations, to address complex product and business challenges. Your contributions will be instrumental in influencing strategy and investment decisions through data-driven insights. You will be expected to drive impact and foster collaboration across teams. Joining Convera means becoming part of a leading data science community focused on professional development and career advancement in data-related fields and beyond.

Your responsibilities will encompass:

• Applying a variety of analytical and statistical approaches to solve complex problems using large datasets.
• Utilizing technical skills in quantitative analysis, experimentation, and data mining to develop business strategies.
• Identifying and measuring business success through goal setting, forecasting, and monitoring key product metrics to track trends.
• Defining, understanding, and testing opportunities and levers for business improvement and roadmap development, informed by your insights and recommendations.
• Partnering with Product, Engineering, and other cross-functional teams to inform, influence, support, and execute product strategy and investment decisions.

We are seeking candidates with:

• A Bachelor's degree in Mathematics, Statistics, a relevant technical field, or equivalent practical experience.
• A minimum of 2 years of professional experience in analytics, proficiency in data querying languages (e.g., SQL), scripting languages (e.g., Python), and/or statistical/mathematical software (e.g., R).
• At least 2 years of experience in solving analytical problems using quantitative methods, understanding complex ecosystems, user behaviors, long-term business trends, and leading data-driven projects from inception to completion, including defining metrics, experiment design, and communicating actionable insights.