C5i is hiring

Job Summary:

Function: 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:

• Develop and implement machine learning models for document detail extraction and data processing. Perform feature engineering to enhance predictive models and data algorithms.
• 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.
• Utilize Python and libraries such as pandas, Numpy, scikit-learn, TensorF10W, and PyTorch for data analysis, processing, and model development.
• Create interactive data applications using Streamlit for various stakeholders.
• Conduct prompt engineering to optimize the Al models’ performance and accuracy.
• Implement chain of thought techniques to improve AI decision-making processes.
• Develop and deploy AI agents to automate and optimize (industry-specific) processes. Collaborate with cross-functional teams to identify and solve complex problems using generative AI and data science techniques.
• Continuously monitor, evaluate, and refine models to ensure performance and accuracy.
• Conduct in-depth research on the latest advancements in generative Al techniques and apply them to real-world business problems.

We are seeking a highly skilled Data Science and Generative AI Specialist with years of experience in machine learning, deep learning, or AI research, with a focus on generative models. The ideal candidate will have strong expertise in data science, machine learning, and generative AI, with specific experience in document detail extraction, feature engineering, data processing using Python, and familiarity with tools such as Streamlit for data app creation. The candidate must also possess advanced skills in prompt engineering, chain of thought techniques, and AI agents to drive our cutting-edge projects forward.

• 2+years of experience in machine learning, deep learning, or AI research, with a focus on generative models.
• Experience with generative models such as GANS (Generative Adversarial Networks), VAEs (Variational Autoencoders), and transformer-based models (e.g., GPT-3/4, BERT, DALL.E).
• Understanding of model fine-tuning, transfer learning, and prompt engineering in the context of large language models (LLMS).
• Knowledge of reinforcement learning (RL) and other advanced machine learning techniques applied to generative tasks.
• Strong programming skills in Python and familiarity with relevant libraries and frameworks.
• Proven experience in document detail extraction and feature engineering.
• 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 Al 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.

Good to Have:

• Experience in the banking/financial services/industry-specific sector.
• Familiarity with cloud-based machine learning platforms such as Azure, AWS, or GCP.
• Proven experience working with OpenAI or similar large language models (LLMs).
• Certifications in Data Science, Machine Learning, or AI.