Senior Data Scientist Lead - (Credit Risk & Analytics)

What You'll Do:

• Guide and empower:Lead and mentor a team of data scientists and analysts, fostering their growth and expertise.
• Uncover hidden insights and business findings:Analyze diverse data sources, including customer data, cases, and stakeholder feedback, to extract valuable insights.
• Bridge the gap:Collaborate with business partners to design and implement data-driven solutions that tackle emerging business challenges.
• Automate for success:Develop intelligent, scalable, and automated solutions using your data science skills.
• Tech-savvy collaboration:Work with other technology leaders and engineers to define requirements and prioritize capabilities based on data analysis and business needs.
• Project ownership:Independently lead analytics and data science projects, setting goals, estimating benefits, and managing timelines.
• Communication is key:Clearly communicate complex findings and actionable outcomes to leadership, peers, stakeholders etc..

Who You Are:

• Data Science Rockstar:4+ years of experience in advanced analytics, statistical modelling, and machine learning.
• Experience in Credit Risk:In-depth understanding of the credit risk lifecycle, from origination to debt collection in the lending domain(b2c).-***Non-Negotiable***
• Tech know-how (must):Proficiency in Python, SQL/NoSQL, ML algorithms, big data concepts, and cloud implementations experience and expertise.
• Always Learning:Continuously seeks out new knowledge, ideas, and feedback to build personal expertise.
• Leadership & Communication:Strong organizational skills, follow-through, and exceptional written, verbal, and interpersonal communication.
• Problem-Solving Powerhouse:High emotional intelligence, can-do attitude, and creative approach to solving problems.
• Self-starter:Takes ownership, drives projects with minimal guidance, and focuses on high-impact work.
• Education:Preference would be given to premium educational institute candidates

Bonus Points:

• Collections and Digital Channels experience.
• Big data and cloud computing experience (Spark, Hadoop, MapReduce, PIG, HIVE).
• Prior Credit Risk and Credit score domain experience.