Associate Data Scientist

• 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

• 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

Benefits

• Meaningful equity to full-time employees

• Performance reviews conducted twice per year, with opportunities to advance

• Collaboration across departments - there’s always an opportunity to work on the projects you care most about

• We encourage people to hone their crafts by taking classes and attending conferences

• We pay 95% of your premiums with top medical, dental and vision plans for you and your family, plus other benefits including FSAs and life insurance

• We match employee retirement contributions dollar-for-dollar up to 4% after your first year

• Flexible hours and a flexible vacation policy

• Remote or in-office options- You are excited to apply your existing expertise in fields such as statistics and computer science to solve mission critical problems

• You are passionate about maintaining the high scientific and engineering standards required to enable your peers

• You are a quantitative thinker who wants to develop further as both a data scientist and an engineer

• Above all, you are a curious and independent problem solver who is motivated to find a place where your skills can have real impact

• 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 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 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

• Experience with SQL and/or Spark

• At least 1 year of professional industry experience, in addition to your academic experience

• Experience using supervised and unsupervised statistical techniques such as regression, classification, and clustering

• Strong programming experience in Python and one or more of the following: Scala/Java or R

• MS or higher in the following areas: Statistics, Mathematics, or Computer Science

• Outstanding analytical skills and the ability to communicate quantitative results to technical and non-technical audiences

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

• Knowledge of data structures and algorithm complexity

• Strong knowledge of Statistics/Probability/Machine Learning including core concepts of hypothesis testing, inference, bias-variance trade-off, regularization, dimensionality reduction etc

• Experience with popular machine learning algorithms such as random forests, Boosting, and neural networks