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Job: Data Scientist
Experience: 2-6 Years
Location: Bangalore
Commitment: Full-Time
Team: Data Science
What we are looking for:
Data Scientist preferably from financial services, large banks/MNCs.
• Own end to end business problems and metrics, build and implement ML solutions
• Design, experiment and evaluate innovative models for predictive learning
• Establish scalable, efficient, and automated process for large scale data analysis, feature creation, model development and deployment
•Exposure/Experience in developing Machine Learning models using various algorithms (logistic/linear, Random Forest. Xgboost ,etc)
• Strong background in business analysis (consumer/business strategy, financial products, pricing, etc.) with very strong data analysis (Sql, excel, etc,) experience
• Strong understanding of how to structure analysis and to solve real world business problems.
• Ability to identify key drivers of business and create KPIs/modeling solutions for the same,
• Expertise in end-to-end model development and model lifecycle management (develop, deploy, monitor) is preferred
• Hands on experience in R or Python is must
• Good business understanding of fintech/personal lending space is preferred
Responsibilities:
You'll work closely with the Product and Risk Team to:
Job ID: 152383447
Skills:
Pyspark, Data Management, Sql, DevSecOps, Software Engineering, Machine Learning Algorithms, Python, Open-Source Programming Languages, Data Preprocessing, Statistics, Artificial Intelligence Systems, Ericsson Business Intelligence and Analytics Competence, Cloud Development
Skills:
probability , statistical concepts , Tableau, Sql, Clustering, Python, Sap Business Objects, regressions, Looker, R-Shiny, R, Microstrategy
Skills:
Github, Pandas, Docker, Python, Data Analysis, Demand Forecasting, Supply Chain Analytics
Skills:
Machine Learning, Sql, Google Cloud, Deep Learning, MLops, Statistical Modelling, Microsoft Azure, Python, AWS, Generative AI, RAG frameworks, vector databases, prompt engineering
Skills:
process mining , Deep Learning, Nlp, Spark, Databricks, Python, data pipelines, unsupervised ML, ML models, supervised ML