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Data Insights Strategist

1 week ago

Bengaluru, Karnataka, India Vivriti Capital Full time

About Vivriti Capital

Vivriti Capital is a pioneering mid-market lender that empowers businesses through customized debt solutions.

Our company operates through two core divisions:

  1. Vivriti Capital Limited: A systematically important Non-Banking Financial Company (NBFC ND-SI) regulated by the Reserve Bank of India (RBI). We have successfully disbursed over $3 billion to 300+ enterprise borrowers, boasting a CRISIL rating of A+.

  2. Vivriti Asset Management: A fixed-income fund manager managing multiple Alternative Investment Funds (AIFs). With over $550 million in commitments from 900+ institutional and private contributors, we have invested more than $600 million across 90+ entities.

https://www.youtube.com/watch?v=5rGfHVlRX0A

The Opportunity

We are seeking a skilled professional to join our team and contribute to our journey as a fast-growing company. If you are passionate about data assets and their value to organizations, this role may be perfect for you.

About the Role

This position involves bringing a culture of data aggregation and analysis to our company, developing consistent and reliable solutions that can be leveraged to scale various business objectives. The ideal candidate will have a knack for identifying solutions in large datasets and converting insights into strategic opportunities for our company.

Key Responsibilities

  1. Scale analytics capabilities across all disciplines and guide our strategic direction towards analytics.

  2. Organize and analyze large, diverse datasets across multiple functions, translating business needs to technical requirements and implementation.

  3. Identify key insights and leverage them to inform and influence product strategy.

  4. Develop proof of concepts to prove or disprove the validity of concepts.

  5. Develop statistical/ML models for various use cases, customer analytics, segmentation, etc., using logistic regression, decision trees, random forests, clustering algorithms, etc.

  6. Ensure model performance, validation, scalability criteria are reviewed regularly with timely updates for proactive adoption to changes in business strategies or portfolio performance.

  7. Partner with the Data engineering team to define the required data pipelines to build and enhance the repository (foundational capability) to build/deploy the various AL/ML algorithms both for batch and real-time use cases.

  8. Utilize effective project planning techniques to break down complex projects into tasks and ensure deadlines are kept.

  9. Support critical business decisions through ad-hoc analysis to uncover new insights in data.