Senior Data Scientist

Role Overview:

We are seeking a highly skilled and experienced Senior Data Scientist with a minimum of 4 years of experience in Data Science and Machine Learning, with a strong focus on NLP, Generative AI, LLMs, MLOps, Optimization techniques, and Agentic AI solution Architecture. In this role, you will play a key role in the development and implementation of AI solutions, leveraging your technical expertise. The ideal candidate should have a deep understanding of AI technologies and experience in designing and implementing cutting-edge AI Agents, workflows and systems. Additionally, expertise in data engineering, DevOps, and MLOps practices will be valuable in this role.

Key Responsibilities:

• Design and implement state-of-the-art Agentic AI solutions tailored for the financial and accounting industry.
• Develop and implement AI models and systems, leveraging techniques such as Language Models (LLMs) and generative AI to solve industry-specific challenges.
• Collaborate with stakeholders to identify business opportunities and define AI project goals within the financial and accounting sectors.
• Stay updated with the latest advancements in generative AI techniques, such as LLMs, Agents and evaluate their potential applications in financial and accounting contexts.
• Utilize generative AI techniques, such as LLMs and Agentic Framework, to develop innovative solutions for financial and accounting use cases.
• Integrate with relevant APIs and libraries, such as Azure Open AI GPT models and Hugging Face Transformers, to leverage pre-trained models and enhance generative AI capabilities.
• Implement and optimize end-to-end pipelines for generative AI projects, ensuring seamless data processing and model deployment.
• Utilize vector databases, such as Redis, and NoSQL databases to efficiently handle large-scale generative AI datasets and outputs.
• Implement similarity search algorithms and techniques to enable efficient and accurate retrieval of relevant information from generative AI outputs.
• Collaborate with domain experts, stakeholders, and clients to understand specific business requirements and tailor generative AI solutions accordingly.
• Establish evaluation metrics and methodologies to assess the quality, coherence, and relevance of generative AI outputs for financial and accounting use cases.
• Ensure compliance with data privacy, security, and ethical considerations in AI applications.
• Leverage data engineering skills to curate, clean, and preprocess large-scale datasets for generative AI applications.

Qualifications & Skills:

• Education – Bachelor’s/Master’s in Computer Science, Engineering, or related field (Ph.D. preferred).
• Experience – 4+ years in Data Science/Machine Learning with proven end-to-end project delivery.
• Technical Expertise – Strong in ML, deep learning, generative AI, RAG, and agentic AI design patterns.
• Programming Skills – Proficient in Python (plus R), with hands-on experience in TensorFlow, PyTorch, and modern ML/data stacks.
• GenAI Frameworks – Experience with LangChain, LangGraph, Crew, and other agentic AI frameworks.
• Cloud & Infrastructure – Skilled in AWS/Azure/GCP, containerization (Docker, Kubernetes), automation (CI/CD), and data/ML pipelines.
• Data Engineering – Expertise in data curation, preprocessing, feature engineering, and handling large-scale datasets.
• AI Governance – Knowledge of responsible AI, including fairness, transparency, privacy, and security.
• Collaboration & Communication – Strong cross-functional leadership with ability to align technical work to business goals and communicate insights clearly.
• Innovation & Thought Leadership – Track record of driving innovation, staying updated with latest AI/LLM advancements, and advocating best practices.

Good to Have Skills:

• Apply trusted AI practices to ensure fairness, transparency, and accountability in AI models.
• Utilize optimization tools and techniques, including MIP (Mixed Integer Programming).
• Deep knowledge of classical AIML (regression, classification, time series, clustering).
• Drive DevOps and MLOps practices, covering CI/CD and monitoring of AI models.
• Implement CI/CD pipelines for streamlined model deployment and scaling processes.
• Utilize tools such as Docker, Kubernetes, and Git to build and manage AI pipelines.
• Apply infrastructure as code (IaC) principles, employing tools like Terraform or CloudFormation.
• Implement monitoring and logging tools to ensure AI model performance and reliability.
• Collaborate seamlessly with software engineering and operations teams for efficient AI model integration and deployment.
• Familiarity with DevOps and MLOps practices, including continuous integration, deployment, and monitoring of AI models.