Spec Analytics Analyst

Client Obsession - Create client centric analytic solution to business problems. Individual should be able to have a holistic view of multiple businesses and develop analytic solutions accordingly. Analytic Project Execution - Own and deliver multiple and complex analytic projects. This would require an understanding of business context, conversion of business problems in modeling, and implementing such solutions to create economic value. Domain expert - Individuals are expected to be domain expert in their sub field, as well as have a holistic view of other business lines to create better solutions. Key fields of focus are new customer acquisition, existing customer management, customer retention, product development, pricing and payment optimization and digital journey. Modeling and Tech Savvy - Always up to date with the latest use cases of modeling community, machine learning and deep learning algorithms and share knowledge within the team. Statistical mind set - Proficiency in basic statistics, hypothesis testing, segmentation and predictive modeling. Communication skills - Ability to translate and articulate technical thoughts and ideas to a larger audience including influencing skills with peers and senior management. Strong project management skills. Ability to coach and mentor juniors. Contribute to organizational initiatives in wide ranging areas including competency development, training, organizational building activities etc. Bachelor's Degree with atleast 3 years of experience in data analytics, or Master's Degree with 2 years of experience in data analytics, or PhD. Hands-on experience in PySpark/Python/R programing along with strong experience in SQL. 2-4 years of experience working on deep learning, and generative AI applications Experience working on Transformers/ LLMs (OpenAI, Claude, Gemini etc.,), Prompt engineering, RAG based architectures and relevant tools/frameworks such as TensorFlow, PyTorch, Hugging Face Transformers, LangChain, LlamaIndex etc., Solid understanding of deep learning, transformers/language models. Familiarity with vector databases and fine-tuning techniques Experience working with large and multiple datasets, data warehouses and ability to pull data using relevant programs and coding. Strong background in Statistical Analysis. Capability to validate/maintain deployed models in production Self-motivated and able to implement innovative solutions at fast pace Experience in Credit Cards and Retail Banking is preferred Strong communication skills Multiple stake holder management Strong analytical and problem solving skills Excellent written and oral communication skills Strong team player Control orientated and Risk awareness Working experience in a quantitative field Willing to learn and can-do attitude Ability to build partnerships with cross-function leaders Bachelor's / master's degree in economics / Statistics / Mathematics / Information Technology / Computer Applications / Engineering etc. from a premier institute For complementary skills, please see above and/or contact the recruiter.
Work with large and complex datasets using a variety of tools (Python, PySpark, SQL, Hive, etc.) and frameworks to build Deep learning/generative AI solutions for various business requirements. Primary focus areas include model training/fine-tuning, model validation, model deployment, and model governance related to multiple portfolios. Design, fine-tune and implement LLMs/GenAI applications using techniques like prompt engineering, Retrieval Augmented Generation (RAG) and model fine-tuning Responsible for documenting data requirements, data collection/processing/cleaning, and exploratory data analysis, including utilizing deep learning /generative AI algorithms and, data visualization techniques. Incumbents in this role may often be referred to as Data Scientists. Specialization in marketing, risk, digital, and AML fields possible, applying Deep learning & generative AI models to innovate in these domains. Collaborate with team members and business partners to build model-driven solutions using cutting-edge Generative AI models (e.g., Large Language Models) and also at times, ML/traditional methods (XGBoost, Linear, Logistic, Segmentation, etc.)

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