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.
Requirements:
We are seeking a highly skilled Data Science, Machine Learning
and Generative AI Specialist
with 5+ years of relevant experience in Advanced Analytics, Statistical, ML model development, deep learning, and AI research. In this role, candidates will be responsible for leveraging data-driven insights and machine learning techniques to solve complex business problems, optimize processes, and drive innovation. The ideal candidate will be skilled in working with large datasets to identify opportunities for product and process optimization and using models to assess the effectiveness of various actions. They should have substantial experience in applying diverse data mining and analysis techniques, utilizing various data tools, developing and deploying models, creating and implementing algorithms, and conducting simulations. Generative AI exposure of advanced prompt engineering, chain of thought techniques, and AI agents to drive our cutting-edge will support candidacy.
Qualifications:
- Bachelors, Masters or Ph.D in Engineering, Data Science, Mathematics, Statistics, or a related field.
- 5+ 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, DALL E).
- 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.
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).
- Experience with deep learning, NLP, or computer vision.
- Experience with big data technologies (e.g., Hadoop, Spark) is a plus.
- Certifications in Data Science, Machine Learning, or 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.
- 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.
- 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.
- 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.
