Technology Lead

Mid / Senior



Meytier Premier Employer

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About This Workplace

Meytier Partner

Scienaptic is the world's leading AI powered Credit Underwriting platform company. Designed by seasoned Chief Risk Officers, its platform is creating industry leading business impact in terms of lifts such as higher approvals (15-40%) and lower credit losses (10-25%) with all the regulatory explainability. Last year alone, we have helped financial institutions evaluate 45 Million consumers and offer credit to over 15 Million. Scienaptic’s clients include Fortune 100 banks, community banks and Fintechs.

This role will enable you to be at the forefront of latest cutting-edge technology and create a significant and visible business impact for Scienaptic as well as its clients. You will be working with some of the best-in-class Coders, AI/ML Scientist and Business Analytics Consultants in an environment which will encourage you to contribute widely to functional and technological aspects.


  •  Manage and lead a team of Engineers and Data Scientist across India and US
  •  Establish and maintain connect with senior stakeholders across clients
  •  Leverage the batch computation frameworks and workflow management platform (Airflow) to assist in building out different data pipelines
  •  Lower the latency and bridge the gap between production systems and data warehouse by rethinking and optimizing our core data pipeline jobs
  •  Pair with client teams to optimize and rewrite business critical batch processing jobs in Spark
  •  Build a framework and tools to re-architect data pipelines to run incrementally  

Skills and Experience:

  • 8-10 years of overall experience; 3-4 years of team leading experience
  • Good understanding of data analysis using SQL queries; Strong hold on Java or Python as a programming language
  • Experience of building libraries and tooling that provide abstractions to users for accessing data
  • Experience of writing and debugging ETL jobs using a distributed data framework (Spark/Hadoop MapReduce)
  • Experience of optimizing the end-to-end performance of distributed systems
  • Ability to recommend and implement ways to improve data reliability, efficiency, and quality.

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