Data Scientist (Risk strategy, SME)

About bluCognition :

bluCognition is an AI / ML based start-up specializing in risk analytics, data conversion and data enrichment capabilities. Founded in 2017, by some very named senior professionals from the financial services industry, the company is headquartered in the US, with the delivery center based in Pune. We build all our solutions while leveraging the latest technology stack in AI, ML and NLP combined with decades of experience in risk management at some of the largest financial services firms in the world.

Our clients are some of the biggest and the most progressive names in the financial services industry. We are entering a significant growth phase and are looking for individuals with entrepreneurial mindset who wants us to join us in this exciting journey.

Position : Data Scientist (Risk strategy, SME)

About the role

As Data Scientist in the credit risk strategy team, you will leverage your creative and critical thinking skills to develop best-in-class risk management strategies that have a meaningful impact on the client’s business. These strategies will support the client’s credit and fraud risk, customer experience, marketing verticals and beyond.

Having you aboard will enable us to stay aligned with market trends by improving the turnaround time for developing and implementing risk strategies, allowing for quicker iterations and broader coverage in addressing business challenges through scientific methods. The core KPIs for this position include additional revenue generated and costs saved from releases. This role also supports compliance, documentation, and knowledge sharing in risk strategies.

What you'll do

• Develop, validate and deploy risk management strategies using a combination of sophisticated data analytics and domain expertise
• Extract and explore data, validate data integrity, perform ad hoc analysis, evaluate new data sources for usage in strategy development
• Maintain robust documentation of approach and techniques used; including objectives, assumptions, performance, weaknesses, and limitations
• Be ready to adapt to new tools / libraries / technologies / platforms
• Actively partner with engineers to validate & deploy scalable solutions
• Collaborate to gather insight from partners across the organization
• Further develop expertise in data science and engineering through self-study, project exposure and guidance of senior team members

What you'll bring