Key Responsibilities and Requirements:

Problem identification and troubleshooting using analytics a variety of difficult engineering problems in field of technical expertise, including serviceability and manufacturability. Works in project teams as subject matter expert to design and develop program methods to consolidate and analyze structured and unstructured, diverse 'big data' sources – eg. sensor and metrology data. Identify data sources and automate collection processes.

Performs statistical and data-mining analysis, using statistical tools like R, Julia, and MATLAB; input and design of data acquisition systems, data structure and database design. Interfaces with internal customers for requirements analysis and compiles data for scheduled or special reports and analysis Works in project teams to develop analytical models, algorithms and automated processes, applying SQL understanding and PHP or Python programming, to cleanse, integrate and evaluate large datasets. Analyze large amounts of information to discover trends and patterns Build predictive models and machine-learning algorithms Supports the timely development of products for manufacturing and process information by applying sophisticated data analytics, understands the business data gathering processes of the business Support system engineering projects that have a substantial mix of electrical, mechanical,  physics, algorithms and software design, and understand the underlying system implications.

Provide remote support to field personnel as required and if needed. Demonstrated experience applying data science methods to real-world data problems

Requirements

Master’s or PhD program in Computer Science, Data Science, Industrial Engg, EE, Physics, or a related field

Student must be in good academic standing at their university, with a GPA of 3.0 or above on a 4.0 scale

Proficiency in Python, R, and MATLAB,

Deep Learning concepts and frameworks – expertise programming in Python data stack, including ML packages such as Scikit-Learn, Tensorflow, Keras, and pyTorch

Experience using business intelligence tools (e.g. Tableau) and data frameworks (e.g. Hadoop)

Desired Skills – Great to Have!

Experience in the fields of quantitative causal inference and counterfactual reasoning, causal reinforcement learning, and the intersection of causal inference and machine learning/reasoning (including computer vision, natural language processing). Process and gain insight from large amounts of multimodal relational data (e.g. time-series, text, images, graphs, spatio-temporal processes).

Proficiency in one or two of the FE frameworks:  JavaScript (Angular), JQuery, XML, Bokeh, pyQT, React

Functional Knowledge

Demonstrates conceptual and practical expertise in own discipline and basic knowledge of related disciplines

Problem Solving

Solves complex problems; takes a new perspective on existing solutions; exercises judgment based on the analysis of multiple sources of information

Impact

Impacts a range of customer, operational, project or service activities within own team and other related teams; works within broad guidelines and policies

Interpersonal Skills

Explains difficult or sensitive information; works to build consensus