Role Overview:

As a Data Scientist at T D Newton, you will lead the development and deployment of sophisticated AI models that address critical business challenges. You will work closely with cross-functional engineering teams, product managers, and senior stakeholders to translate complex requirements into robust, production-grade machine learning pipelines. Your contributions will directly influence the companys product roadmap, enabling the delivery of intelligent, data-driven features that enhance user experience and optimize business outcomes across our core platforms.

Key Responsibilities:

- Architect and deploy end-to-end machine learning models to solve complex business problems, ensuring high performance and scalability for enterprise-level applications.

- Lead the integration of Generative AI and Large Language Models (LLMs) into existing workflows to automate processes and improve the quality of automated insights.

- Collaborate with engineering and product teams to refine data collection strategies, ensuring the integrity and quality of datasets used for model training.

- Mentor junior data scientists and provide technical guidance on best practices in Python development, model evaluation, and deployment strategies.

- Communicate technical findings and model performance metrics to non-technical stakeholders to drive data-informed decision-making across the organization.

Required Skillset:

Basic/Essential Qualifications:

- Designing, developing, and deploying advanced analytics and Machine Learning solutions across Markets use cases such as pricing, risk, forecasting, client analytics, liquidity, operational efficiency, or surveillance.

- Identification, collection, extraction, and preparation of data from diverse internal and external sources, ensuring high data quality and analytical integrity.

- Building and maintaining scalable data pipelines and analytical workflows in partnership with Data Engineering and Platform teams.

- Developing predictive, statistical, and machine learning models and translating analytical outputs into actionable business insights.

- Applying GenAI techniques such as natural language processing, document intelligence, retrieval-augmented generation (RAG), and decision-support use cases.

- Strong stakeholder engagement with senior business, technology, risk, and control partners, with the ability to communicate complex concepts clearly and influence outcomes.

- Operating effectively in regulated, enterprise environments with strong awareness of data governance, model risk management, and control frameworks.

- Leading complex, cross-functional initiatives and guiding other data scientists and specialists as a senior individual contributor or technical lead.

Desirable Skillsets:

- Experience within Banking, Finance, or Capital Markets environments.

- Strong programming skills in Python and SQL, with experience using modern analytics and machine learning frameworks.

- Hands-on experience delivering analytics or ML solutions into production environments, including performance monitoring and lifecycle management.

- Familiarity with Enterprise Data Platforms, cloud-based analytics ecosystems, and MLOps / AI governance practices.

- Ability to balance strategic thinking with hands-on technical execution.