Assistant Manager - Data Science - Insurance Predictive Analytics Domain

This position is for Assistant Manager (Data Science) role in the Insurance Predictive Analytics domain for one of our client. The key responsibility will be to support the client's P&C Insurance Analytics team in undertaking various Analytics and Data Science projects.

The job responsibilities include the following:

- Clearly Setting Project Objectives with the Client - Take initiatives to identify opportunities and develop problem statements to be worked upon.

- Data Extraction, Cleansing and Manipulation - Handle large volume of data, research for variables and work with structured/unstructured data.

- Data visualization - develop solution with tools like Plotly and dash to visualize the distribution of the data and provide inferences

- Predictive Modelling - Development of models using ML techniques

- Model Deployment - built front end tools and deploy the solution using flask ,Rest API to integrate with the Predictive Model in real time

- Model Documentation - Clear and Detailed Documentation of the modelling procedures/ analyses.

- Presentation - Able to present the Analysis in a clear, concise and effective manner

About You

Personal Qualifications

- Deep understanding of ML techniques and coding languages like python, SQL, R

- Strong communicator and leadership skills.

- Should have experience in client handling.

- Experience in working in a multi-cultural environment

- Questions existing processes, suggests improvements

Required Education and Skills

- Academic Background in Engineering, Masters in technology/Statistics

- Strong Analytical skills with 3-7 years of experience - Hands on experience in Data modelling and data analytics tools like, SQL, Python and good knowledge in advanced analytics / Machine learning / statistical / data mining / Text mining techniques in Regression or classification

- Knowledge of coding languages like Python ,SQL, R, SAS

- Experience in Data and Model pipelines like Meta-flow, Airflow, ML flow is preferable