Staff Data Scientist

Overview

The Innovation team develops game changing technologies and experiments that redefine and disrupt our current product offerings. You’ll be building and prototyping algorithms and applications on top of the collective financial data of 60 million consumers and small businesses. Applications will span multiple business lines, including personal finance, small business accounting, and tax.

You thrive on ambiguity and will enjoy the frequent pivoting that’s part of the exploration. Your team will be very small and team members frequently wear multiple hats.

In this position you will have close collaboration with the Innovation & Advanced Technology Group’s engineering and design teams, as well as the product and data teams in business units. Your role will range from research experimentalist to technology innovator to consultative business facilitator. You must be comfortable partnering with those directly involved with big data infrastructure, software, and data warehousing, as well as product management.

What you'll bring

• MS or PhD in an appropriate technology field (Computer Science, Statistics, Applied Math, Operations Research, etc.).
• At least 8+ years of experience with data science for MS or at least 4+ for PhD.
• Experience in modern advanced analytical tools and programming languages such as R or Python with scikit-learn.
• Efficient in SQL, Hive, or SparkSQL, etc.
• Application building using LLM’s
• Comfortable in Linux environment
• Experience in data mining algorithms and statistical modeling techniques such as clustering, classification, regression, decision trees, neural nets, support vector machines, anomaly detection, recommender systems, sequential pattern discovery, and text mining.
• Solid communication skills: Demonstrated ability to explain complex technical issues to both technical and non-technical audiences.
  
  

Preferred Additional Experience

• Apache Spark
• The Hadoop ecosystem
• Java
• HP Vertica
• TensorFlow, reinforcement learning
• Ensemble Methods, Deep Learning, and other topics in the Machine Learning community
  
  

How you will lead

• Perform hands-on data analysis and modeling with huge data sets.
• Apply data mining, NLP, and machine learning (both supervised and unsupervised) to improve relevance and personalization algorithms.
• Work side-by-side with product managers, software engineers, and designers in designing experiments and minimum viable products.
• Discover data sources, get access to them, import them, clean them up, and make them “model-ready”. You need to be willing and able to do your own ETL.
• Create and refine features from the underlying data. You’ll enjoy developing just enough subject matter expertise to have an intuition about what features might make your model perform better, and then you’ll lather, rinse and repeat.
• Run regular A/B tests, gather data, perform statistical analysis, draw conclusions on the impact of your optimizations and communicate results to peers and leaders.
• Explore new design or technology shifts in order to determine how they might connect with the customer benefits we wish to deliver.
• Work with a cross functional team of data scientists and product managers to develop strategies for our products that serve billions of people and millions of businesses
• Mentor other data scientists in the team