Opportunity for Cluster Manager - Payments - Data Science

Job Description

* Duties and Responsibilities - Planning Engage with the Business Teams to understand their pain points and data needs. Create roadmap of specific tasks & initiatives that the team needs to work on. Cascade the tasks and initiatives to the team. Liaise with various stakeholders including: Issuance & Acquiring Business Heads. Business intelligence Engage with stakeholders to make sure dashboards are utilized correctly Leverage Big Data tools and techniques to build innovative solutions using appropriate modelling techniques Assist in driving the future development of prediction modelling infrastructure in terms of process performance and testing Deliver data insights and BI that are accurate and informative to enable the business to make data-driven content and learner experience decisions Create data based actionable insights that can be leveraged by Product & COE team. Define Build & Track Hourly Daily Monthly KPIs and work on the plan on how to improve the same. Initiate Campaigns to drive KPIs to measure the efficiency of the campaigns. Manage ad-hoc data insights Trend analysis requests for Product & COE team. Conduct thorough quality assurance of the data feeds as well as the analytical capabilities Develop compelling summaries that visualize the data through narrative dashboards to clearly tell a succinct story in easy to understand ways Demonstrate analytic curiosity and a desire to understand challenges to contribute to matrix team reviews and strategic discussions for insight discovery Expertise in designing and testing experiments. Knowledge and experience with using GIS tools for spatial data analysis Work with large complex data sets whilst solving challenging business problems independently and across teams Machine Learning Manage accuracy of Model inventory ongoing model performance monitoring model change control and participate in discussions with Business Heads on model performance Keep up with the latest developments in CCB/industry in terms of modeling techniques (advanced AI/ML methodologies) products markets models risk management practices and industry standards. Participate and actively contribute to various process enhancement initiatives for innovations/automation. Using machine learning tools to select features create and optimize classifiers Carrying out preprocessing of structured and unstructured data * Major Challenges - Co-ordination between multiple teams (Business IT EDW) and external vendors to enable task delivery Providing rightful solutions to Business problems with data driven insights Co-ordination with IT teams for any new development. Daily sync with EDW team for data quality checks. Enabling controls on data sanity and build data engineering pipeline. Collaborate with client stakeholders to understand and frame business requests determine how best to leverage machine learning and advanced analytic methods (Ensemble models Decision trees and Neural networks; Operations research; Statistical modeling such as multivariate techniques) to support business objectives Efficiently construct develop and implement the models in a variety of modeling tools achieving highly accurate models * Key Decisions / Dimensions - Define User Segments and analyze performance of Campaign Create relevant user segments while analyzing Data Solutions to Business data problems Delivery Timeline related. Adapting & Implementing industry best practices Data Analysis and Value creation Create insights from predictive statistical modeling mathematical knowledge tools and techniques to solve complex problems and deliver value Translate business issues into specific requirements to develop analytic solutions and identify appropriate data to support the solution Collaborate with Business teams to address business issues and influence change using strategy industry and analytical skills , * a) Qualifications Bachelor s Degree in computer science, Math, Physics, Engineering, or related quantitative field or Post-graduate from a reputed institute with a minimum 5 years of experience as a Data Scientist in Fintech/Payment/Relevant Industry. Work Experience Minimum of 5 - 7 Years of experience Leading Data Scientists & Data Analysis Experience in implementing machine learning techniques and algorithms, such as, Naive Bayes, SVM, Decision Forests. Deep learning implementation preferably CNN & Open CV Experience with common data science toolkits, such as Python (Pandas, NumPy, Sci-kit, SciPy etc.) Great communication skills (ability to convert data language to business language) Experience with data visualization tools, such as Power BI, PlotLy, Matplotlib etc. Strong bias for action & driving results in a high-performance environment. Should have strong knowledge on Database management, ETL, Data Architecture & database schema. Proficiency in using query languages such as SQL, Spark SQL is mandatory. Experience with NoSQL databases, such as MongoDB, Cassandra will be a plus

Keyskills :
salesretaildata sciencebig datainsurancedata qualitybusiness developmentpower bi