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Quantifind

Quantifind

www.quantifind.com

1 Job

99 Employees

About the Company

Quantifind helps some of the world's biggest banks catch money laundering and fraud. Quantifind also works with government agencies to use the same platform to uncover criminal networks and combat election tampering. Unlike other players in this space, Quantifind delivers results as software-as-a-service (SaaS) with consumer-grade user experiences. Quantifind is a data science technology company whose AI platform uncovers signals of risk across disparate and unstructured text sources. In financial crimes risk management, Quantifind's solution uniquely combines internal financial institution data with public domain data to assess risk in the context of Know Your Customer (KYC), Customer Due Diligence (CDD), Fraud Risk Management, and Anti-Money Laundering (AML) processes. Today these compliance processes are burdened by ever-increasing regulatory responsibilities and an expectation of frictionless transactions. Legacy technologies demand increasingly more human resources as the operations expand; Quantifind's solution offers a way to cut through the inefficiency and enhance effectiveness simultaneously.

Listed Jobs

Company background Company brand
Company Name
Quantifind
Job Title
Associate Data Scientist
Job Description
**Job Title** Associate Data Scientist **Role Summary** Develop and deploy machine‑learning solutions that analyze large unstructured financial data to detect fraud, money‑laundering, and other compliance risks. Work within an agile, cross‑functional team to prototype models, build production‑ready pipelines, and integrate advanced AI tools, including LLMs, into the company's SaaS platform. **Expectations** - Minimum 1 year of industry data‑science experience. - Master’s or higher in Statistics, Mathematics, or Computer Science. - Strong analytical mindset with ability to communicate quantitative findings to both technical and business audiences. - Self‑driven, independent problem solver comfortable with rapid prototyping and iterative improvement. **Key Responsibilities** - Prototype complex ML models and evaluate performance using hypothesis testing and cross‑validation. - Design, implement, and maintain ETL pipelines (SQL, Python, PySpark) for large, heterogeneous datasets. - Train supervised and unsupervised models (random forests, boosting, neural nets) and apply NLP methods (topic modeling, embeddings, text classification). - Deploy models into production Scala codebase following software‑engineering best practices. - Leverage LLMs for data‑labeling, entity extraction, and other scaling tasks. - Collaborate with Product Managers and Platform Engineers to translate business problems into technical solutions. - Participate in code reviews, documentation, and knowledge sharing. **Required Skills** - *Programming*: Python, PySpark, SQL, Scala; ML libraries (scikit‑learn, XGBoost, PyTorch/TensorFlow); NLP tools (spaCy, Hugging Face). - *Statistics/Machine Learning*: hypothesis testing, inference, bias‑variance trade‑off, regularization, dimensionality reduction. - *Data Engineering*: ETL design, big‑data processing, data quality. - *Soft Skills*: analytical thinking, clear communication, agile collaboration, independent execution. **Required Education & Certifications** - Master’s or PhD in Statistics, Mathematics, or Computer Science (or equivalent). - Relevant certifications (e.g., AWS Certified Machine Learning, Microsoft Certified: Azure Data Scientist Associate) are advantageous but not mandatory.
Palo alto, United states
Hybrid
Fresher
10-02-2026