2+ Gradient boosting Jobs in Bangalore (Bengaluru) | Gradient boosting Job openings in Bangalore (Bengaluru)

• Use data to develop machine learning models that optimize decision making in Credit Risk, Fraud, Marketing, and Operations
• Implement data pipelines, new features, and algorithms that are critical to our production models
• Create scalable strategies to deploy and execute your models
• Write well designed, testable, efficient code
• Identify valuable data sources and automate collection processes.
• Undertake to preprocess of structured and unstructured data.
• Analyze large amounts of information to discover trends and patterns.

Requirements:

• 2+ years of experience in applied data science or engineering with a focus on machine learning
• Python expertise with good knowledge of machine learning libraries, tools, techniques, and frameworks (e.g. pandas, sklearn, xgboost, lightgbm, logistic regression, random forest classifier, gradient boosting regressor, etc)
• strong quantitative and programming skills with a product-driven sensibility

• 3-5yrs of practical DS experience working with varied data sets. Working with retail banking is preferred but not necessary.
• Need to be strong in concepts of statistical modelling – particularly looking for practical knowledge learnt from work experience (should be able to give "rule of thumb" answers)
• Strong problem solving skills and the ability to articulate really well.
• Ideally, the data scientist should have interfaced with data engineering and model deployment teams to bring models / solutions to "live" in production.
• Strong working knowledge of python ML stack is very important here.
• Willing to work on diverse range of tasks in building ML related capability on the Corridor Platform as well as client work.
• Someone with strong interest in data engineering aspect of ML is highly preferred, i.e. can play dual role of Data Scientist as well as someone who can code a module on our Corridor Platform writing robust code.

Structured ML techniques for candidates:

1. GBM
2. XgBoost
3. Random Forest
4. Neural Net
5. Logistic Regression