(Associate up to 5 years / Consultant 5-9 years / Senior Consultant 10 years & above)

 The Data scientist will be involved in building cutting edge algorithms and working with state-of-art data science and machine learning tools such as Spark, TensorFlow and will have mastery in any number of analytic platforms like Python, R, SAS, SQL, Tableau, Qlik etc.

Applicant with strong proficiency with statistical modeling, data mining, machine learning, artificial intelligence and working with large scale datasets, distributed Big Data Platforms / Hadoop for a variety of advanced analytics solutions across the Bank ranging from recommendation engines, Propensity models, customer segmentation, Graph models, pricing and more.

Responsibilities

• Employ statistics/data science/machine learning techniques to extract, clean and interpret the data into insights from structured and unstructured data. Web scraping may be also a part of the work scope in data extraction.
• Work closely with Project Manager, Visualisation Developer, Data Engineer, UXD and Data Analyst to build scalable data-driven products
• Work in an Agile Environment that practices Continuous Integration and Delivery
• Work with large data sets (e.g. Predictive, Fraud/Anomaly Detection, Text Analytics, Customer Segmentation)

Requirements

• Bachelors/Masters/ Doctorate in Statistics, Analytics, Applied Mathematics, Operation Research, or equivalent quantitative fields preferred. Strong mathematical and statistics
• Minimum 5 years of experience in industry (ideally ecommerce, technology, banking, telecom, retail) and/or academia with demonstrated track record of innovative research and insight generation and implementation of insights into tools/processes delivering front end business
• Should have fair understanding of using data mining, machine learning techniques and recommendation Engines on large amount of data, building and implementing various statistical
• Strong understanding of technology tools especially those related to analytics, data &
• Strong written and oral communication
• Should be able to perform data analysis using Python / R / Spark / SAS and/or any other related programming language
• Familiar with data modelling, data access, and data storage infrastructure like Data Mart, Data Lake, and Data Warehouse
• Proficient in general data cleaning and transformation (e.g. SQL, pandas, R, etc)
• Familiarity with Analytics and Big Data platforms like Teradata, Hadoop, Hive
• Proficient with processing data from open-standard file format or data interchange format (e.g. XML, JSON)
• Proficient in advanced analytics techniques (machine learning algorithms, statistical methods: regression, classification)