Associate Data Scientist

Quantifind•Washington, DC

3d•$100,000 - $130,000•Hybrid

About The Position

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 money laundering committed by internationally sanctioned entities. 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 through Machine Learning driven solutions that resolve for both accuracy and relevance. To help you succeed, we provide a supportive environment that fosters collaboration between teams and team members, where learning and professional growth are considered a key part of your success, and of ours. We offer a flexible work environment with a family friendly work-life balance. Quantifind is seeking to fill an Associate Data Scientist position on our Data Science team. The Data Science team works closely with the Product Management team and Platform engineers to anticipate company needs and quickly put state-of-the-art mathematical tools into the hands of end users. Our development team is centered in Palo Alto, California, and we have technology hubs in Atlanta, Georgia, and in Washington, D.C. We are currently using a hybrid mix of working from home and in the office. The DC team is primarily remote, with periodic in person touchpoints at a physical office space in DC.

Requirements

MS or higher in the following areas: Statistics, Mathematics, or Computer Science
At least 1 year of professional industry experience, in addition to your academic experience
Outstanding analytical skills and the ability to communicate quantitative results to technical and non-technical audiences
Strong knowledge of Statistics/Probability/Machine Learning including core concepts of hypothesis testing, inference, bias-variance trade-off, regularization, dimensionality reduction etc.
Supervised and unsupervised statistical techniques such as regression, classification, and clustering
Experience with popular machine learning algorithms such as random forests, Boosting, and neural networks
Strong programming experience in Python and one or more of the following: Scala/Java or R
Experience with SQL and/or Spark
Knowledge of data structures and algorithm complexity

Nice To Haves

Experience with Applied NLP methods such as topic modeling, text classification, word embeddings, and named entity extraction is a strong plus

Responsibilities

Collaborating with fellow team members and key stakeholders, such as, Product Managers, Platform Engineers to explore ideas, test hypotheses, and prototype solutions.
Working in an agile environment breaking down complex problems into smaller manageable tasks with the help of your teammates and manager.
Leveraging SQL, Python and PySpark to analyze large unstructured data sets to answer key business questions, inform next steps or set up modeling tasks.
Writing complex pipeline code often involving NLP enrichment steps to process large data sets.
Training machine learning models to solve complex problems.
Productionizing models in a Scala codebase following software engineering best practices.
Validating models using standard and custom performance metrics.
Leveraging Large Language Models (LLMs) to scale up various data science tasks (for example: data labeling, extracting structured information, …)
Participating in code reviews.