About Us

We're building AI-native tools that harness the power of large language models (LLMs) to help customers solve high-impact, real-world problems in financial crime compliance. Our platform integrates structured and unstructured data, enabling rapid prototyping, seamless collaboration, and fast iteration with a strong focus on end-to-end delivery.

As we scale, we're looking for a data scientist who thrives at the intersection of research, implementation, and cross-functional collaboration — someone who can own analytical work end-to-end and contribute directly to customer-facing solutions.

What You’ll Do

Lead data exploration and analysis on large scale financial crime datasets — including sanctions, PEP (Politically Exposed Persons), and adverse media data — to uncover patterns, identify false positives/negatives, and drive feature improvements.
Develop and evaluate agents and rule-based models by running experiments, validating hypotheses, and fine-tuning thresholds to improve alert efficiency.
Build and deliver production-ready API integrations — coordinating with software engineers and product teams to ensure components are properly integrated, tested, and merged.
Conduct customer-focused data studies across multiple enterprise clients (e.g., financial institutions) to benchmark model performance, assess data quality, and propose data driven solutions to reduce investigation loads.
Prototype and iterate quickly — using PySpark, Jupyter notebooks, and Python to explore data, build reproducible pipelines, and generate insights that inform product decisions.
Investigate and resolve product issues in collaboration with engineering and product teams.
Contribute to R&D on emerging techniques — including graph-based approaches (GNNs, graph embeddings) for transaction monitoring, LLM-based feature exploration, and RAG-based models.
Communicate findings clearly through well-organized Jupyter notebooks, internal documentation, and stakeholder presentations, translating complex analytical results into actionable business insights.

What We’re Looking For

Based in Singapore or London (remote-first team; flexible working environment).
Bachelor's or Master's degree in Data Science, Computer Science, Statistics, or a related field.
Minimal 3 years of hands-on experience delivering data science projects, ideally in financial crime compliance, name screening, or AML/KYC domains.
Strong proficiency in Python (data manipulation, modelling, pipeline development) and SQL / Spark SQL for large-scale data querying and transformation.
Hands-on experience with PySpark or similar distributed data platforms.
Familiarity with NLP techniques, and entity resolution concepts.
Experience working with LLMs or RAG-based models for information extraction or classification tasks is an advantage.
Solid understanding of data quality assessment, including profiling, anomaly identification, and merging logic across complex multi-source datasets.
Comfortable working in Git, Docker, Linux, and collaborative development workflows (including code reviews and pull requests).
Strong analytical and problem-solving skills — able to investigate ambiguous data issues, form hypotheses, and validate findings rigorously.
Good communication skills — able to document findings in a structured and reproducible manner (Jupyter notebooks, Confluence), and present results clearly to both technical and non-technical stakeholders.
A mindset of ownership and curiosity: you take initiative, ask the right questions, and follow through to delivery.

Originally posted on Himalayas

Data Scientist at Silent Eight