Description

Responsibilities

• Perform hands-on data analysis and modeling with huge data sets
• Apply data mining, NLP, and supervised/unsupervised ML to design, improve, and evaluate relevance and personalization algorithms across customer journeys.
• 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
• Engineer and iterate on features from underlying data, combining domain understanding with statistical rigor to propose, evaluate, and refine feature sets that improve model performance over successive cycles.
• 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

 Qualifications:

• BS, MS, or PhD in an appropriate technology field (Computer Science, Statistics, Applied Math, Operations Research, etc.)
• 4+ years of industry experience with Data Science and Machine Learning 
• 4+ years of experience in modern advanced analytical tools, programming languages - Python with scikit-learn
• Efficient in SQL, Hive,  SparkSQL, etc.
• 4+ years of experience in applied machine learning and statistical modeling (e.g., classification, regression, decision trees, neural networks, recommender systems, sequential pattern discovery, and text mining), with hands-on experience in deep learning and embedding-based representations for NLP/language understanding, recommendation, and personalization.
• Solid communication skills: Demonstrated ability to explain complex technical issues to both technical and non-technical audiences

Key Skills

Education

Bachelor's degree