Manager Analytics
2 weeks ago
The Role:
The Risk analytics team at Razorpay is looking for a self driven Senior manager to turbo charge Machine learning driven Risk Intelligence sub-function. The Risk analytics team comprises passionate problem solvers who share a common vision of enabling decision-making through data-driven strategies. These strategies enable Razorpay to offer the best risk and high order acceptance rate to merchants as well as enable Razorpay to avoid high operational cost and brand value loss. A successful person in this role is self-starter, has a bias for action and is passionate about not only developing new solutions but also who has an implementation first mindset.
Roles and Responsibilities:
You will develop a strong understanding of the Risk OKRs and the environment in which we operate. You will work closely with cross functional partners in product, business, analytics and data engineering to define key initiatives against Risk OKRs. You will bring clarity to ambiguous use cases, aligning multiple stakeholders to identify the right problem statement, objectives and then building out the solution concepts. You will apply excellent problem solving skills to independently scope the ML problems and define an end to end picture of model conceptualisation, development, deployment and experimentation/consumption. You would thrive in a highly dynamic environment, consistently meeting deadlines and delivering exceptional performance with limited supervision. Take responsibility for skill-building within the organisation (training, process definition, research of new tools and techniques, etc). Stay updated with the latest tools and technologies to improve upon existing Machine learning capabilities. Define the business and product metrics to be evaluated, work with engineering on data instrumentation, create and automate self-serve dashboards to present to relevant stakeholders leveraging tools such as Tableau, Qlikview, Looker, etc. Developed a clear understanding of the qualitative and quantitative aspects of the product/strategic initiative and leverage it to identify and act upon existing Gaps and Opportunities.Qualifications:
5 to 10 years experience in Analytics & Data Science, experience in a Tech Ecosystem. Exposure to fraud, risk or insurance is a plus. Proficient in identifying AI/ML related opportunities from complex business problems and leading the design and development of effective solutions in close collaboration with engineering, product, and business teams Hands-on with Machine Learning development with some knowhow of deploying models in production. You possess expert level knowledge of basic machine learning techniques : regression, classification, clustering, model metrics and performance (AUC, ROC, precision, recall and their various flavors). Exposure to Deep Learning applications and proficiency with tools like TensorFlow, Theano, Torch, Caffe are advantageous Exceptionally skilled in SQL and other relational databases, with expertise in PySpark or Spark SQL. Competent in utilizing NoSQL databases and familiarity with GraphDBs like Neo4j is a plus. Hands-on experience in working with large scale structured, semi-structured and unstructured data and various approaches to preprocessing/cleansing data, dimensionality reduction. Extensive experience collaborating with MLEs (Machine Learning Engineers) and adept at leveraging MLOps tools. Demonstrates a deep understanding of deployment lifecycles and effectively utilizes these tools. Previous exposure to CI/CD deployment is highly beneficial. Skilled in constructing compelling data narratives through comprehensive exploratory data analysis (EDA) and statistical inference. Fundamental knowledge of experimentation design is essential Proficient in building frameworks for conducting model Root Cause Analyses (RCAs) using analytical and interpretability tools Experienced in risk management of different lending and payment products across the lifecycle of merchant cash flows and transaction behaviors. Prior experience of working with cross functional stakeholders involving engineering, product and business is a plus. Work experience in Consumer-tech organizations would be a plus.-
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