Machine Learning / Data Scientist

Proprius - San Francisco, CA

Full-time

San Francisco, CA

Real Estate

About the position

Our client is seeking a Machine Learning Engineer to join their existing ML team, focusing on developing and refining predictive applications. The role involves leveraging large data sets to optimize products and processes, utilizing various data mining and analysis methods, and collaborating with stakeholders to drive business results through data-driven insights.

Responsibilities

• Work with stakeholders throughout the organization to identify opportunities for leveraging data to drive business solutions.
• Mine and analyze data from databases to drive optimization and improvement of product development, marketing techniques, and business strategies.
• Assess the effectiveness and accuracy of new data sources and data gathering techniques.
• Develop custom data models and algorithms to apply to datasets.
• Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting, and other business outcomes.
• Coordinate with different functional teams to implement models and monitor outcomes.
• Develop processes and tools to monitor and analyze model performance and data accuracy.

Requirements

• Strong with Statistics and can code in either R, Python, Java, and Scala.
• Experience with designing and building using micro-services architectural pattern, web APIs using dotnet core & C#.
• Experience and passion for simulations, optimization, neural networks, artificial intelligence (deep learning and machine learning).
• Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, GIT, SQL, etc.
• Able to understand statistical solutions and execute similar activities.
• Experience in data wrangling and advanced analytic modeling.
• Strong communication and organizational skills and has the ability to deal with ambiguity while juggling multiple priorities and projects at the same time.
• Experience visualizing/presenting data for stakeholders using: Seaborn, Business Objects, D3, ggplot, etc.
• Ability to investigate the feasibility and data requirements necessary to develop an ML solution for a given problem.
• Ability to design, build and test production-ready ML-based products while interpreting and explaining the basis for predictions generated by ML models.

Nice-to-haves

• Knowledge and experience using one or more of the following, or similar, machine learning software frameworks: CAFFE, Torch 7, Keras, and Tensorflow.
• Experience building production-ready NLP or information retrieval systems.
• Hands-on experience with NLP tools, libraries, and corpora (e.g. NLTK, Stanford CoreNLP, Wikipedia corpus, etc.)