Senior Business Data Scientist

Overview

Intuit’s Small Business Group (SBG), a global team serving the needs of over 2 million small businesses and self-employed. We are rethinking how to power prosperity around the world. This means dramatically rethinking how we can enable small businesses and individuals to run their businesses with confidence and save time by using both our cloud solutions and our app partners.

The Data Analytics & Science team drives user growth and retention using product insights and marketing channel optimization for the Small Business Group. We are an exciting, growing, and fun team that works with industry-leading analytics tools, techniques, and best practices.

Marketing Analytics group is looking to expand its capabilities in the data engineering, machine learning, and data analytics realm to work closely with analysts, engineers, and marketers.

What You'll Bring

• Bachelor’s degree in Information Systems, or Computer Science, Engineering, Applied Math or equivalent work experience. Master’s preferred
• Experience in data mining algorithms and statistical modeling techniques such as clustering, classification, regression, decision trees, neural nets, support vector machines, recommender systems, sequential pattern discovery, and NLP text mining
• Experience in modern advanced analytical tools and programming languages such as R or Python with scikit-learn
• Experience working with Hive SQL, SparkSQL, and Linux
• Solid communication skills: Demonstrated ability to explain complex technical issues to both technical and non-technical audiences
• Can-do attitude, hands-on approach, passionate about data
  

How You Will Lead

• Influence key stake holder including marketing and engineer partners with working solution and procedures for multi-channel personalization capabilities and ML-model based targeting
• Apply data mining, NLP, and machine learning (both supervised and unsupervised) to improve relevance and personalization algorithms
• 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
• 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 and build data pipelines
• Work side-by-side with product managers, software engineers, and designers in designing experiments and minimum viable products