Lead Data Scientist

Lead Data Scientist

  • Experience in database such as SQL, Hadoop, NoSQL, and Massively Parallel Processing (MPP) databases.
  • Proficient and ability to code and develop prototypes in programming languages in Python, PySpark, Flink, flume etc
  • Build complex analytical concepts/models related to Fraud
  • Demonstrated analytic agility.
  • Should have relevant work experience and has worked on Fraud Models in the earlier organisations.
  • Experience in delivering solutions using large datasets and data wrangling both structured and unstructured data.
  • Comfortable with relational and non-relational databases and API development
  • Hands on Experience in deep learning, neural networks etc
  • Ability to work in a fast-paced and deadline driven environment
  • Strong work ethics like sense of collaboration and ownership, result orientation, being    team player
Key Skills: 
big data
Data Science
Machine Learning
Deep Learning
Job Description: 

Analyse effectiveness of fraud models to constantly improve tools, procedures, and workflows that minimize fraud/risk and enhance customer experience.

Should have analytical bent of mind.

Uses best practices to understand the data and develop fraud statistical, machine learning techniques to build models that address business needs.

Collaborates with the team in order to improve the effectiveness of fraud business decisions through the use of data and machine learning/predictive modelling.

Ability to stretch, learn and deliver with stricter Timelines and non-negotiable SLA TAT’s.

Analysis of data to identify fraud and provide insights into fraud solution and fraud workflow analysis and contribute towards the success of our fraud analytics initiatives

Transform data into insights, to identify and quantify opportunities to reduce fraud and false positive into a positive business impact.

Use and leveraging internal and external Fraud tools as part of our Fraud operations (e.g., Python, SQL, Spark)

Performing and Leveraging data science methods such as SVM, neural nets, regression, decision trees, and ensemble techniques to develop proprietary algorithms

Job Location: 
Job Department: 
Job Experience Type: 
Job experience in months: 
Job experience max: 
11 years
Job Experience min: 
5 years