- Minimum 3 years of professional related work experience.
- 1-3 years quantitative analytical experience, including conducting hands-on analytics projects using generalized regression models, Bayesian methods, random forest, gradient boosting, neural networks, support vector machine, clustering, and similar methodologies.
- Proficiency skill in hands-on data mining and modeling projects with Python, R, and SQL.
- Master's degree or higher in Mathematics, Computer Science , Engineering, Operations Research, Statistics or other related discipline.
- Strong consultative acumen and ability to understand complex analytical solutions.
- Ability to communicate complex quantitative analysis in a clear, precise, and actionable manner.
- Ability to create new ideas for analytical solutions to address customer's business issues.
Preferred Skills and Experience:
- 4+ years of professional experience as a data scientist or statistical modeler in at least one of the following: identity and fraud, credit risk, telecommunications, financial services, payment, ecommerce, B2B or B2C, marketing, insurance, or security analytics arena.
- 4+ years working with Python and SQL.
- Experience with state of the art machine learning algorithms such as deep neural networks,support vector machines, boosting algorithms, random forest etc. preferred
- Experience conducting advanced feature engineering and data dimension reduction in Big Data environment is preferred.
- professional experience as a data scientist or statistical modeler in identity and fraud, credit risk, telecommunications, financial services, payment, ecommerce, B2B or B2C, marketing, insurance, or security analytics arena is a plus.
- Strong SQL skills in Big Data environment (Hive/ Impala etc.) a plus.
- Proficient with any programming languages such as Python , Java, Scala a plus
- Experience working with very large datasets, knowledge of distributed computing tools (Hadoop Streaming, MapReduce, Spark) a plus.
- Exposure to Visualization tools such as Tableau a plus.
- Extensive knowledge in fraud prevention methods and detection tools a plus.
- Strong knowledge of credit bureau data and business problems in financial services and/or telecommunications a plus.
Top Required Skill Sets -
- 3+ years of professional experience as a data engineer
- 3+ years working with Python and SQL.
- Experience with state-of-the-art machine learning algorithms such as deep neural networks, support vector machines, boosting algorithms, random forest etc. preferred
- Experience conducting advanced feature engineering and data dimension reduction in Big Data environment is preferred
- Strong SQL skills in Big Data environment (Hive/ Impala etc.) a plus
- Things that would stand out on resume -
- 1- Masters Degree in Computer Science & Data Science
- 2- Previous Company - Any Bank, Ecommerce