Description
The Emerging Tech Standard Delivery Team drives the adoption of advanced technologies and develops enterprise-ready solutions for D&A Operations. You will partner with Operations and Technology teams to design data-driven solutions that deliver clear business and customer value.
Responsibilities & Key Accountabilities:
- Own the product vision for an industry-leading data management framework, covering data acquisition, cleaning, transformation, and related workflows.
- Support Lead Data Scientist on end-to-end AI/ML lifecycle: problem definition, design, experimentation, deployment, and continuous improvement.
- Define an optimized user experience for financial analytics data pipelines, unifying multiple tools and services into an integrated workflow.
- Partner with analytics domain experts to understand business data needs and translate them into a practical platform and framework strategy.
- Lead the execution of this strategy in close collaboration with engineering and project delivery teams.
- Communicate product goals, progress, and achievements to product, engineering, business leaders, sales, proposition, support, and other internal stakeholders.
- Stay informed on financial analytics market trends to maintain a forward-looking, future-ready strategy.
- Continuously learn and adapt to emerging technologies and techniques in financial analytics.
- Develop and deploy production-grade AI models and data-driven solutions aligned with strategic objectives.
- Tune and optimize AI models to improve accuracy, performance, and scalability.
- Evaluate third-party AI capabilities using both functional and non-functional criteria.
- Establish and enforce coding standards to ensure a maintainable, robust codebase.
- Collaborate with Software Engineers and cross-functional teams to define requirements, set direction, and shape development roadmaps.
- Hands-on experience in data science, including Natural Language Processing, Large Language Models, prompt engineering, and Retrieval-Augmented Generation (RAG).
- Skilled in training, testing, and validating deep learning models as well as classical machine learning models.
- Ability to design and implement evaluation frameworks for automation solutions based on business requirements.
- Experience building solutions and onboarding workloads on AWS or other cloud platforms.
- Expertise in web crawling and scraping techniques, entity identification and extraction, and advanced pre- and post-processing methods using emerging technologies such as prompt engineering.
- Proficient in processing structured, semi-structured, and unstructured data from PDFs and scanned documents.
Requirements:
- 6-8 years of experience in data science, analytics, or statistical modeling roles.
- Strong foundation in data analytics, feature engineering, NLP, predictive modeling, LLMs, and RAG workflows.
- Expertise in Python and core ML/DL libraries (TensorFlow, PyTorch, Scikit-learn), with demonstrated experience in developing and productionizing AI models.
- Proven ability to apply strong statistical analysis skills, including probability and applied statistics, to model development and evaluation.
- Experience with Git-based version control and CI/CD pipelines (GitLab/GitHub runners).
- Strong understanding of relational databases, large-scale data processing, and statistical programming.
- Familiarity with MLOps principles and experience deploying solutions on cloud platforms (AWS and/or Azure).
- Hands-on experience with commercial or open-source data management tools.
- Excellent communication and presentation skills, capable of simplifying complex technical concepts for both technical and business audiences.
- Strong influencing and collaboration skills, supporting cross-team execution and alignment.
- Ability to diagnose and resolve complex technical and business problems using deep analytical and industry knowledge.
- Ability to set and uphold coding standards.
- Hands-on experience with leading commercial or open-source data management tools.
- Excellent written, verbal, and presentation skills, with the ability to present to peers, senior management, and cross-functional partners.
- Broad business awareness and understanding of relevant internal business relationships.
Benefits:
- Healthcare
- Retirement planning
- Paid volunteering days
- Wellbeing initiatives