The role focuses on developing AI and machine learning models and pipelines, emphasizing generative AI, large language models (LLMs), and predictive modeling. The individual in this position collaborates closely with stakeholders to understand data requirements and product specifications, enabling data-driven decisions and effective product design. They also work alongside software engineers and developers to deliver comprehensive solutions. A key aspect of this role involves rapidly acquiring proficiency with new tools and technologies, supported by a solid foundation in machine learning/AI and basic programming skills.
The role requires practical experience in designing, developing, managing, and maintaining systems while working with large datasets. Expertise in common machine learning and AI frameworks, coupled with a strong understanding of AI fundamentals (including deep learning, regression, classification, and clustering algorithms), is essential. Proficiency in retrieval-augmented generation pipelines and the evaluation of LLM-driven use cases is also expected.
Key Responsibilities:
Lead AI initiatives by delivering integrated solutions that merge software engineering, statistical analysis, and machine learning for complex applications.
Conduct deep, analytical experiments using appropriate techniques to achieve significant incremental improvements.
Prepare high-quality reports and presentations to communicate hypotheses and insights that influence organizational decisions.
Identify, transform, and explore data, becoming the subject matter expert on available datasets.
Facilitate seamless collaboration between engineers, product analysts, and other organizational teams.
Qualifications and Skills
5 to 7 years in relevant roles, including leadership experience.
At least 3 years of experience in a data scientist capacity.
Postgraduate degree in Computer Science, or equivalent experience in the field.
Proficiency in machine learning tools such as PyTorch, TensorFlow, scikit-learn, and XGBoost.
Strong statistical analysis and programming skills (e.g., Python, R, or SQL).
Experience designing, implementing, testing, and deploying machine learning models.
Hands-on experience with LLM-based pipelines, including retrieval-augmented generation and prompting techniques.
Familiarity with tools such as LangChain, LlamaIndex, Haystack, Azure AI Studio, and vector databases is advantageous.
Database management via SQL or tools like Azure Data Factory; familiarity with big data frameworks (e.g., Hadoop, Spark) is a plus.
Knowledge of MLOps fundamentals, including orchestration tools, cloud computing, and observability systems.
Preferred Industry Knowledge:
Understanding of revenue cycle management, collections, or the financial sector is desirable but not mandatory.
General Competencies:
Ability to work in fast-paced, team-based, and independent environments.
Strong communication skills, both written and verbal.
Commitment to continuous learning and adaptation to emerging technologies.