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Job Location: Menlo Park, CA
Role: Data Scientist V
Location: Remote
Job Term: Long-term Contract
Approved Remote Locations: Denver, CO, New York, NY, Houston, TX, Los Angeles, CA, and San Diego, CA.
Client’s Enterprise Engineering team develops and maintains scalable products that power the enterprise. Enterprise Product Applied Research team is composed of applied quantitative and computational experts using machine learning, statistics and operations research to bring in step-level improvements in efficiency and scalability across the entire suite of enterprise products.
As a member of Enterprise Engineering, you will play a key role in reimagining productivity by shipping transformative products that serve diverse aspects of the enterprise.
Responsibilities
1 Build pragmatic, scalable, and statistically rigorous scientific solutions for large scale enterprise problems by leveraging or developing state of the art machine learning and optimization methodologies on top of Client’s unparalleled data infrastructure
2 Work cross-functionally to define problem statements, collect data, build analytical models and deploy them at scale.
3 Build and maintain data driven machine learning models, optimization models, experiments and forecasting algorithms.
4 Apply excellent communication skills in order to develop cross-functional partnerships and spread scientific best practices
5 Be able to work both independently and collaboratively with other scientists, engineers, designers, UX researchers, and product managers to accomplish complex tasks that deliver demonstrable value to Client’s Enterprise Products.
6 Think creatively, proactively, and futuristically to identify new opportunities that will grow the enterprise product’s long-term roadmap and bring productivity gains for the enterprise
Minimum Qualifications
1 Ph.D. or Masters degree in quantitative field (e.g. computer science, engineering, operations research, electrical engineering, statistics, mathematics and related fields),
2 2+ years of industry or graduate research experience solving analytical problems and building models using quantitative, statistical or machine learning approaches
3 Experience with machine learning, natural language understanding, computer vision, statistics or mathematical programming tools and techniques
4 Experience performing data extraction, cleaning, analysis and presentation for medium to large datasets
5 Experience with at least one programming language (i.e. Python, R, Java, or C++)
6 Experience writing SQL queries
7 Experience with scientific computing and analysis packages such as NumPy, SciPy, Pandas, Scikit-learn, dplyr, or ggplot2
8 Experience with machine learning libraries and packages such as PyTorch, Caffe2, TensorFlow, Keras or Theano
9 Experience with statistics methods such as forecasting, time series, hypothesis testing, classification, clustering or regression analysis
10 Experience initiating and driving applied research projects to completion with minimal guidance
11 Experience communicating scientific work in a clear and effective manner.
Preferred Qualifications
1 Experience working with distributed computing tools (Hadoop, Hive, Spark, etc.)
2 Experience in object oriented programming such as Python, C++, Java
3 Experience using deep learning, natural language processing or computer vision in a production environment
4 Proficiency in algorithmic complexity
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