[Apply in 3 Minutes] Senior Manager

Job Description The Data & Analytics team is responsible for integrating new data sources, creating data models, developing data dictionaries, and building machine learning models for Wholesale Bank. The primary objective is to design and deliver data products that assist squads at Wholesale Bank in achieving business outcomes and generating valuable business insights. Within this job family, we distinguish between Data Analysts and Data Scientists. Both roles work with data, write queries, collaborate with engineering teams to source relevant data, perform data munging (transforming data into a format suitable for analysis and interpretation), and extract meaningful insights from the data. Data Analysts typically work with relatively simple, structured SQL databases or other BI tools and packages. On the other hand, Data Scientists are expected to develop statistical models and be hands-on with machine learning and advanced programming, including Generative AI. Key Responsibilities: - Extract and analyze data from company databases to drive the optimization and enhancement of product development and marketing strategies. - Analyze large datasets to uncover trends, patterns, and insights that can influence business decisions. - Leverage predictive and AI/ML modeling techniques to enhance and optimize customer experience, boost revenue generation, improve ad targeting, and more. - Design, implement, and optimize machine learning models for a wide range of applications such as predictive analytics, natural language processing, recommendation systems, and more. - Implement advanced data augmentation, feature extraction, and data transformation techniques to optimize the training process. - Deploy generative AI models into production environments, ensuring they are scalable, efficient, and reliable for real-time applications. - Use cloud platforms (AWS, GCP, Azure) and containerization tools (e.g., Docker, Kubernetes) for model deployment and scaling. - Create interactive data applications using Streamlit for various stakeholders. - Conduct prompt engineering to optimize AI models performance and accuracy. - Stay up-to-date with the latest advancements in data science, machine learning, and artificial intelligence to bring innovative solutions to the team. - Communicate complex findings and model results effectively to both technical and non-technical stakeholders. - Continuously monitor, evaluate, and refine models to ensure performance and accuracy. - Conduct in-depth research on the latest advancements in generative AI techniques and apply them to real-world business problems. Qualifications: - Bachelor's, Master's or Ph.D in Engineering, Data Science, Mathematics, Statistics, or a related field. - 10+ years of experience in Advance Analytics, Machine learning, Deep learning. - Proficiency in programming languages such as Python, and familiarity with machine learning libraries (e.g., Numpy, Pandas, TensorFlow, Keras, PyTorch, Scikit-learn). - Experience with generative models such as GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and transformer-based models (e.g., GPT-3/4, BERT, DALLE). - Understanding of model fine-tuning, transfer learning, and prompt engineering in the context of large language models (LLMs). - Strong experience with data wrangling, cleaning, and transforming raw data into structured, usable formats. - Hands-on experience in developing, training, and deploying machine learning models for various applications (e.g., predictive analytics, recommendation systems, anomaly detection). - Experience with cloud platforms (AWS, GCP, Azure) for model deployment and scalability. - Proficiency in data processing and manipulation techniques. - Hands-on experience in building data applications using Streamlit or similar tools. - Advanced knowledge in prompt engineering, chain of thought processes, and AI agents. - Excellent problem-solving skills and the ability to work effectively in a collaborative environment. - Strong communication skills to convey complex technical concepts to non-technical stakeholders.