Job description
Role: Data Scientist / Risk Strategy
Experience: 3+ Years
Location: Remote
Skills Required: SQL, Python, R, Databricks, Credit Risk Strategy building, Credit Risk Solutions
About bluCognition:
BluCognition is an AI/ML based company specializing in risk analytics, data conversion and data enrichment capabilities. Founded some very named senior professionals from the financial services industry, the company is headquartered in the US, with the delivery centre 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 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
Degree in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred
3+ years of Data Science experience
2+ years in financial services
Experience building and implementing risk strategies in production
Deep understanding of segmentation techniques such as decision trees
Experience in banking sector with exposure to risk management analytics
Proficient with Python
Proficient with SQL
Practical experience using Spark is a plus
Understanding of statistical modeling techniques is a plus
Technical understanding of algorithm complexity, probability & statistics
Self-driven with an aptitude for independent research & problem-solving
Ability to multi-task in a fast-paced environment is essential
