In this role, you will:

• Develop, implement, and calibrate various analytical models.
• Perform highly complex activities related to financial products, business analysis and modeling.
• Perform basic statistical and mathematical models using Python, R, SAS, C++ and SQL.
• Perform analytical support and provide insights regarding a wide array of business initiatives.
• Work individually or as part of a team on data science projects and work closely with business partners.
• Perform Data wrangling activities and develop statistical/machine learning models using various techniques (supervised, unsupervised, semi-supervised) and technologies including but not limited to Python, R, SAS, Spark, H2O, Aster etc.
• Provide solutions to business needs and analyze work flow processes to make recommendations for process improvement in risk management
• Collaborate and consult with peers, colleagues, managers and regulators to resolve issues and achieve goals.

Required Qualifications, US:

• 2+ years of Quantitative Analytics experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
• PhD in statistics, mathematics, physics, engineering, computer science, economics, or quantitative field; or a Masters degree in the above areas

Required Qualifications, International:

• Experience in Quantitative Analytics, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education.
• BS/BA degree or higher in a quantitative field such as applied math, statistics, engineering, physics, accounting, finance, economics, econometrics, computer sciences, or business/social and behavioral sciences with a quantitative emphasis.
• 1-3 years of relevant experience.
• Experience in various aspects of Machine Learning such as data analysis, feature engineering, feature extraction, model training and validation is a must.
• Experience in at least one of supervised, unsupervised, semi-supervise learning and time series analysis.
• Strong practical experience with Python.
• Exposure to SQL, Deep-learning, and Artificial intelligence techniques.
• Good command over MS Office tools – PowerPoint, Excel and data management.

Desired Qualifications:

• Strong understanding of data wrangling and preparation techniques both in single and multithreaded systems in Pandas, Numpy, RDD and spark data-frames.
• Excellent command over supervised, unsupervised and semi-supervised techniques including but not limited to Random Forest, GBM, Ridge-Lasso-ElasticNet, XGboost etc. Time-series techniques like Arima (and the family), Arch, Garch etc.
• Working expertise in Tensorflow, Keras or Pytorch would be added advantage.
• Working expertise on PySpark, H2O would be an added advantage
• Working expertise on Finance or customer analytics would be an added advantage.
• Excellent understanding of model metrics including AUC, ROC, CAP-curve, F-statistics etc. with clear understanding of how model performance is tuned
• Strong programing skills.
• Expertise in multiple analytic tools : R, Python, SAS.
• Big Data skills – Aster, Hadoop, SPARK, H20 and various big data distributions like Hortonworks and MapR.
• Experience on non-structured data analysis – NLP, Text mining, Image/Voice processing, digital analytics.

Job Expectations:

• Person would be required to work individually or as part of a team on data science projects and work closely with business partners across the organization.
•  He/she would be developing statistical/machine learning models using various techniques (supervised, unsupervised, semi-supervised) and technologies including but not limited to SAS, R, Python, Spark, H2O, Aster etc.
• Work closely with data engineers, BI and UI specialists and deliver top notch analytical solution for the bank.
• Define business problem and translate it into analytical problem.
• Adapt at attracting, hiring and retaining top notch data science talents and build world class team.
• Help create conducive environment for nurturing and growing talents.