Data Scientist

Skills

10 to 12 years of relevant experience and 6+ years of experience in data science machine learning quantitative analytics (Mathematics Statistics or Operational Research etc) roles Master’s degree in computer science Statistics or a related field (Mathematics Operational Research Data Science) Must have extensive experience in Feature Engineering – Data Wrangling Feature Reduction Feature Selection and Feature Creations Experience in Building and validating statistical machine learning and other advanced analytics models. Experience in Regression (multiple Logistic etc) Classification (Decision Tree Random Forest XGBoost etc) and Time series Forecasting models (ARIMA) Segmentation NLP Deep Learning and Graph Analytics (AWS Neptune). Experience in Data Mining Python SQL and familiarity with ML technologies Experience in using ML Libraries. Working Experience in Domino Data Lab AWS Sagemaker Snowflake are a plus Experience using business intelligence tools (e.g. Tableau) and data frameworks (e.g. Hadoop) Excellent problem-solving analytical skills and attention to detail with the ability to identify patterns trends and anomalies in data. Ability to write code that is easy to follow well documented and commented where necessary to explain logic (high code transparency). Strong communication and collaboration skills with the ability to effectively interact with technical and non-technical stakeholders.

Description

1. Strong expertise and experience in Feature Engineering – Must have handled millions of records and thousands of columns of data and has done Feature Reduction, Feature Selection, Sample Selection, Feature Creation etc

2. Must have extensive experience in Prediction Models – specifically XGBoost Models

3. Having knowledge in Graph DB and Graph Analytics is a plus – AWS Neptune is preferred

4. Should be strong in python and ML libraries and packages for model development.

Responsibilities

• Gathers, interprets, and manipulates structured and unstructured data to enable analytical solutions for the business.
• Selects the appropriate modeling technique and/or technology with consideration to data limitations, application, and business needs.
• Build various ML Models within the Model guidelines and framework.
• Consults with peers for guidance, as needed.
• Translates business requirements into specific analytical questions, build ML Models and present model outcomes to non-technical business colleagues.
• Consults with Data Engineering, IT, the business, and other internal stakeholders to deploy analytical solutions