Senior Machine Learning Scientist, User Behavior

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

Picsart is looking for a Machine Learning Scientist to join our User Behavior team and help build, scale, and manage state-of-the-art machine learning algorithms that power Picsart. Our “striving for excellence” applied research mindset is focused on leveraging the most cutting-edge AI technologies for the constant improvement of our users' experience.

Responsibilities

• Develop and scale algorithms and mathematical models that significantly improve our current ability to make decisions based on qualitative and quantitative behavioral data
• Work on building Recommendation Systems algorithms, Predictive models, NLP based solutions, Monetization optimization techniques, etc.
• Work as a team player to improve data science methods and be a positive influence on team advancement
• Measure and continue to improve the predictive accuracy of models

Required Qualifications

• BS or MS degree in Machine Learning, AI, Computer Science, Statistics, Applied Mathematics or related technical fields
• Experience with state-of-the-art techniques in machine learning algorithms, deep neural networks especially related to NLP and Recommendation Systems
• Experience in deep learning frameworks such as Tensorflow, Pytorch, Theano and Caffe
• Experience with data science tools including Python scripting, numpy, scipy, pandas, pyspark, matplotlib, scikit-learn, jupyter notebooks, bash scripting, Linux environment
• Experienced in computer science fundamentals such as object-oriented design, data structures, and algorithm design
• Experience with SQL and relational databases
• Experience with working in a distributed, cloud-based computing environment (e.g., Hadoop or Spark) is a plus
• Experience with working with noisy/buggy behavioral data is a plus
• Knowledge and solid understanding of theoretical foundations of A/B testing and practical knowledge of designing robust tests is a plus
• Familiarity with statistics and understanding of different distributions and the conditions under which they’re valid
• Ability to analyze and visualize data and make rigorous statements about what can or cannot be concluded
• Strong background in linear algebra, calculus, and probability theory
• Strong Python coding, strong engineering practices, debugging/profiling skills, familiarity with multi-threaded programming
• Advanced written and verbal English communication skills.