As an Associate Data Scientist, you will report to the Data Science Manager, as well as continuously collaborate with key stakeholders across the business to solve the most important technical problems.

Responsibilities

• In collaboration with Data Product Managers, work with cross-functional partners (Product Development, Marketing, Sales, Customer Success, etc.) to understand & document business requirements including objectives, estimated costs & benefits, inputs, accuracy, latency, scale, and governance constraints

• Collaborate with Data Engineers, Software Engineers, and other business partners to identify, gather, cleanse, and organize data sets needed for analyses & modeling projects

• Perform exploratory data analyses using appropriate techniques (descriptive statistics, visual analytics, clustering, transformations, etc.) to understand, distill, and communicate the information content of data sets, and to extract features that are appropriate for formulation of metrics or as inputs to data models

• With guidance from seasoned Data Scientists, participate in inferential, predictive, prescriptive, causal, and other statistical analyses including hypothesis testing & forecasting to answer business questions, improve customer engagement, and drive business growth

• Assist seasoned Data Scientists to design, train, and evaluate machine learning and AI models while adhering to best practices including model selection, validation, bias/variance tuning, performance assessment, sensitivity analysis, dimensionality reduction, etc.

• Collaborate with seasoned Data Scientists & Machine Learning Engineers to design and implement machine learning & AI models as scalable & robust solutions that can be deployed into production at scale, and help develop monitoring metrics to detect non-stationary behavior, anomalies, and degradation in production

• Follow our governance & development standards, including processes & frameworks for logging experiments, code & model quality standards, documentation, and source controlling artifacts

• Clearly document & present your work and informational materials at the appropriate level of detail to your team & business partners

• Maintain sharp professional knowledge & skills, and stay abreast of new developments in data science, machine learning, & AI

• Generate intellectual property and business ideas that could generate value for the company, such as new use cases and approaches to using data

• Ability to work effectively from your remote location using modern collaborative tools running on a company-provided MacBook Pro

Required Experience & Skills

• Completed BS, MS, or PhD in Computer Science, Mathematics, Statistics, Engineering, or other quantitative field

• Strong programming skills in Python, an understanding of core computer science principles, and experience with Python data manipulation frameworks such as Pandas & PySpark

• Strong data manipulation skills in SQL, and good understanding of relational database design

• Broad knowledge of basic computational statistics and good understanding of theoretical fundamentals of statistics

• Thorough and broad knowledge modeling and training techniques for machine learning & AI, as well as best practices for ensuring robustness and performance

• Experience with frameworks and libraries for machine learning & AI such as scikit-learn, HuggingFace, PyTorch, Tensorflow/Keras, MLlib, etc.

• Talent and skills for creating clear data visualizations for both technical & non-technical audiences

• Strong interpersonal and verbal communication skills

• Ability to work effectively from your remote location using modern collaborative tools running on a company-provided MacBook Pro

Preferred Experience & Skills

• Experience as a Data Scientist applying statistical methods and machine learning as an individual contributor to solve business problems

• Experience designing, training, and evaluating machine learning models that have been deployed into production at scale

• Ability to apply statistical techniques for experiment design (e.g., A/B, multi-cell testing), causal inference methods, and time series analysis/forecasting

• Experience with data warehouses (e.g., dimensional modeling), data lakes/lakehouses, and other data architectures

• Familiarity with Data Engineering, DataOps, and MLOps principles and tools

• Knowledge in domains such as recommender systems, fraud detection, personalization, and marketing science (e.g., attribution, customer LTV, propensity, uplift models)

• Experience with deep learning architectures and frameworks (e.g., PyTorch, TensorFlow/Keras)

• Familiarity with Large Language Models (LLMs), other generative AI modalities, and how they are applied in production

• Knowledge of vector databases, knowledge graphs, and other approaches for organizing & storing information

• Publications in data science, machine learning, AI, and/or other data-related topics