Job Summary:
We are looking for an experienced and innovative Data Scientistto join our global business intelligence & strategy team to design and build intelligent applications using Large Language Models (LLMs) and machine learning models to monitor our platform, detect anomalous activity, perform root cause analysis, and generate automated, natural language insights for both our internal business leaders and our clients.
Key Responsibilities:
- Design and develop applications using LLMs to analyze business data to derive actionable insights.
- Build AI applications to solve business cases, such as detecting abnormalities in customer behavior, business performance and operational metrics.
- Implement AI-driven root cause analysis tools that identify underlying issues, such as algorithmic misbehavior, or system faults.
- Use LLMs to summarize complex insights in natural language, producing interpretable outputs for non-technical stakeholders.
- Integrate AI outputs into internal dashboards or alerting systems used by management teams.
- Collaborate closely with data engineers and business stakeholders to align AI solutions with real-world needs.
- Fine-tune and deploy open-source or proprietary LLMs (e.g., GPT, Claude, Mistral) for business-specific use cases.
- Contribute to the development of a scalable AI platform capable of supporting multiple real-time and batch inference use cases.
Required:
- Bachelor&aposs or Master&aposs degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.
- 3+ years of experience in AI/ML engineering, with proven work in NLP and LLM-based applications.
- Strong coding skills in Python, including experience with libraries like Transformers (Hugging Face), LangChain, PyTorch/TensorFlow.
- Experience building anomaly detection or real-time alerting systems.
- Familiarity with time-series data analysis and financial data structures.
- Ability to translate technical AI output into clear business insights.
Preferred:
- Background in Fintech industries, such as Crypto, Trading firm or Bank .
- Experience with LLM deployment in production (APIs, latency optimization, prompt engineering).
- Exposure to MLOps tools (e.g., MLflow, Docker, CI/CD pipelines for ML).
