Associate Data Scientist

Quantifind•Palo Alto, CA

13h•$100,000 - $130,000•Hybrid

About The Position

Who You Are You are a quantitative thinker who wants to develop further as both a data scientist and an engineer. You are skilled at finding the precise mathematical kernels of real-world problems and want to bring that talent to bear on the business questions facing the world’s leading financial services companies. You are excited to apply your existing expertise in fields such as statistics and computer science to solve mission critical problems. You are excited to work at a fast-paced startup where you will have a chance to expand your scientific and engineering skills to new areas. You share Quantifind’s commitment to winning together, and are eager to see your coworkers build on the technical foundations you will be creating. You are passionate about maintaining the high scientific and engineering standards required to enable your peers. Above all, you are a curious and independent problem solver who is motivated to find a place where your skills can have real impact. Who We Are 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. What You'll Be Doing: Members of the Data Science team prototype and build complex Machine Learning solutions, improve and refine existing products through data-driven development cycles, and support our customers extract the maximum value out of Quantifind's Graphyte platform through proofs of concepts (POC) and purpose built solutions as necessary. We work closely with the Product Management and Platform teams to anticipate company needs and quickly put state-of-the-art Data Science and AI tools into the hands of end users.

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.
Experience using 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 is a strong plus, including topic modeling, text classification, word embeddings, and named entity extraction

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 research directions, or set up modeling tasks.
Designing, writing, and maintaining ETL pipelines for integrating large and complex datasets.
Experimenting with novel ways to use existing data-based solutions to support customers’ unique challenges.
Training machine learning models to solve complex problems.
Implementing Data Science solutions in our Production Scala codebase following software engineering best practices.
Leveraging Large Language Models (LLMs) to scale up various data science tasks (for example: data labeling, extracting structured information, …)
Participating in code reviews.