Urgent Candidates for Deputy National Lead - Payments - Technology

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. Engage in model validation activities - evaluate conceptual reasonableness of assumptions reliability of inputs completeness of testing correctness of implementation associated with development & use of the model. Perform additional model review activities such as reviewing proposed enhancements to existing models extensions to scope of usage for existing models or providing specific approvals. Evaluate the risk posed by ML models and suggest ways to mitigate such risks Liaise with various stakeholders including: Issuance & Acquiring Business Heads. Business intelligence Supervise the team in looking at the availability and feasibility of data for specific business needs. Where data is not available work with the IT team to determine the right data source and establish the data pipeline. Guide the team in understanding the specific data templates and dashboards required by the business teams. Review/create the dashboards created by the team and discuss with business to finalize Break down work into tasks allocate to team member and share updates with stakeholders Engage with stakeholders to make sure dashboards are utilized correctly Engage with IT team to ingest new data into EDW Monitor regularity and accuracy of data flow to the business teams. 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 Work with large complex data sets whilst solving challenging business problems independently and across teams Collaborate with internal business stakeholders to identify priorities for the developers and engineers from an AI perspective Machine Learning & Deep 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 Experience in Solutioning Business requirements into fully operationalized AI / ML Projects Experience in leading data science engagements * Duties and Responsibilities - Team management and coaching Participate in selection process to identify the right talent for positions within the team. Determine individual training needs and development plans to build expertise and enhance skills in the team. Mentor team towards achieving Organization goals Set objectives conduct reviews and close appraisal processes for the team as per timelines To groom internal teams to deliver successful outcomes and to take on additional responsibilities. Lead a team of experienced analytic professionals in the design development and implementation of custom analytic solutions and deliverables for both external and internal customers. * 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 Design and implement end-to-end solutions using Machine Learning Optimization or other advanced technologies and manage live deployments Construct and deliver written reports of the analytic findings in a variety of formats (reports PPT including visualization of data and findings) formulating recommendations and effectively presenting the results to potential non-analytic audience * 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 Deliver large-scale programs that integrate processes with technology to help clients achieve high performance Create foundational data capabilities by leveraging existing data in addition to constantly learning about new technologies. Design and implement new predictive solutions based upon business needs and requirements. Financial Dimensions While the project will take two quarters to deliver the overall GMV expected from Payments business over the next 3 years is above INR 2 lac Crores. Other Dimensions Total Team Size: 6-10 members Number of Direct Reports: 6 Number of Indirect Reports: 0 Number of Outsourced employees: 1 Number of locations: NA , * 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 10 years of experience as a Data Scientist in Fintech/Payment/Relevant Industry. Work Experience Minimum of 10 - 14 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. Proficiency in using query languages such as SQL, Spark SQL is mandatory. Experience with NoSQL databases, such as MongoDB, Cassandra will be a plus Good, applied statistics skills, such as distributions, statistical testing, regression, etc. Good scripting and programming skills (Python/R) with OOPs implementation

Keyskills :
big datapower bideep learningdata analysisdata sciencedata qualitydata flow