Software Engineer - Analytics

Job Description

•  Create analytically focused presentations based on frameworks and with guidance of the senior team.  
• Extract, summarize and analyze data, identify key insights for presentation.
• Provide comprehensive analytics support to partner teams. 
• Ask clarifying questions to understand the objectives and decisions that are informed by the analysis. 
• Utilize descriptive and predictive analytics to discover new insights and build data products. 
• Create visualizations that elegantly represent complex information. 
• Communicate analysis to stakeholders and executives in verbal, visual, and written media. 

We are looking for

• Education: A Bachelors degree or higher, preferably in Business Analytics, Data Science, Economics, Experimental Psychology, Mathematics, Operational Research, Information Science, Computer Science, Engineering, Statistics.
• Experience: 1 to 4 years

Skills

• Understand the payments and/or retail banking industry desired not mandatory.
• Good data sense with ability to connect the data dots to form solid and credible thesis and storylines.
• Good business acumen with ability to structure the real-world problems into quantitative framework to yield proper data science approaches.
• Strong communication skills. 
• Fluency with Office tools (PPT, Word, Excel, etc.)
• Strong logic building & ETL capabilities to extract and transform data.
• Fluency with data, analytics, and visualization technologies (Microsoft Power BI, R, Python, VBA, etc.)
• Creativity. 
• A storyteller, with a knack for communicating concepts in presentations.
• Some experience executing analytics projects.
• Understand the payments and/or retail banking industry.
• Natural curiosity.
• Bias toward action.
• Commitment to accuracy, honesty and ethics. 
• Knowledge of analytics techniques such as general linear model, ANOVA, decision trees, linear regression etc. 
• Fundamental understanding of hypothesis testing & campaign design. 
• Fundamental understanding of standard predictive and classification models; when to use and how to interpret results.