AVP - Data Scientist.
Roles and Responsibilities:
• Building end-to-end data pipelines for ML models, other data driven solutions such that the pipeline is directly usable for deployment/implementation.
• Building and maintaining data pipelines: data cleaning, transformation, roll-up, pre-processing, etc.
• Building/developing data insight solutions various teams like Credit, Collections, Distribution, Vigilance, HR, etc.
• Building automation solutions using Python, SQL, Docker, etc. as required.
• Database management and automation.
• Working experience on Linux server to installation and configuration of software (related to data science and AI domain), service creation, basic shell scripting etc.
• Rest API development and development/management using Docker and related cloud technologies.
Technical Skills
Must have
• Primary skill set:
• High Proficiency in Python coding along with good knowledge of SQL (joins, nested query, etc.)
• Data analysis experience. Understand and identify the data points and data acquisition mechanism for structured and unstructured data (text/json/xml) for machine learning data pipeline.
• Knowledge of using Python Libraries such as Pandas, sqlalchemy (or other Python SQL related libraries), [good to have: matplotlib, numpy, scipy, scikit-learn, nltk].
• Working knowledge of GIT repositories (any of the Github, Gitlab etc.)
• Experience in Rest API developments using any of Django, Flask, FastAPI etc. (this will be highly appreciated) and Deployment of API on cloud using Docker (this will be highly appreciated).
• Hands on experience on Linux OS/platform to install software, Python packages, create services, docker creation, automate and run the code/script.
•Data management skill sets:
• Ability to understand data models and create the ETL jobs using.
Python scripts.
• Automate regular data acquisition, application process etc. using Python scripts.
Good to have (must be open to learning if doesnt have already)
• Other useful skills
• Should be able to work on problems independently or with less support
• Concept of bigdata and Spark (PySpark) knowledge
• Cloud experience (AWS/Azure/GCP)