Analyst/Senior Analyst - BFS

About the job

As a Analyst or Senior Analyst - BFS, you will work with Clients in Banking & Financial Services, & help translate business problems into unambiguous analytics problem statements and design analytical frameworks to address them. On a typical work day, you could be doing one or more of the following: • Collaborate with the team of data scientists and engineers to plan and drive the execution of business solutions. • Contribute to client presentations, reports, QBRs etc. • Handle client discussions in structured project settings. Domain Understanding: • Awareness of the Banking regulatory landscape. • Awareness of common AI and automation use cases in BFS (fraud detection using ML, credit scoring, robo-advisors, chatbots). Can identify basic applications of analytics in operations. • Knowledge of the concepts in any or multiple use cases like customer segmentation, CLTV, Customer attrition and retention, next best product/cross sell and has had contributed hands on to at least a part of any of these projects if not end to end. Involvement in at least any one of Model Validation, Model Performance Monitoring and Model Development is desired. Required Skills & Experience: • 1-3 years of relevant experience in Analytics projects in BFS domain for any geography. • At least 1 year of experience working for global Banks or financial services in the area of either Credit, Fraud, Marketing, Operations etc.. on AI or analytics engagements. • Strong communication & experience in client communication. • Awareness of storytelling with PPT & has effectively communicated with clients to understand their basic requirements, communicate status updates, or escalation calls. • Hands-on experience in preparing Powerpoint / Google Slides presentations & presenting them to key stakeholders. • Hands-on experience with Excel/ Google Sheets to review, sanitize, manipulate data. • Hands-on experience of querying data using SQL on traditional RDBMS databases, and/or big data environments such as Hadoop/Hive/Spark. • Ability to analyse and visualize data one or more tools such as Tableau, PowerBI. • Intermediate experience in DS/ML/AI programming tool of Python. • Exposure to low-code no-code tools GPT, CLausde, Lovable, Gemini. • Appreciation of different types of Statistical / Machine Learning / AI techniques, when and why they are used. • Interpret results from a business viewpoint (the expectation is not a data scientist expert-level proficiency, but a clear conceptual understanding). • Ability to leverage analytics to solve structured or unstructured problems and interpret results.

Requirements

• Analytics
• Client communication
• Data analysis
• Statistical techniques
• Machine Learning

Preferred Technologies

• Analytics
• Client communication
• Data analysis
• Statistical techniques
• Machine Learning