Data Scientist

Battelle•Columbus, OH

7d•Hybrid

About The Position

Battelle delivers when others can’t. We conduct research and development, manage national laboratories, design and manufacture products and deliver critical services for our clients—whether they are a multi-national corporation, a small start-up or a government agency. We recognize and appreciate the value and contributions of individuals from a wide range of backgrounds and experiences and welcome all qualified individuals to apply. Battelle’s Trust and Assurance Cyber Analytics Department is responsible for developing unique embedded security solutions for government and industrial customers. We are seeking a Data Scientist to work in our Columbus, Ohio location. As a Data Scientist, you will work with a diverse group of people from many quantitative disciplines such as physics, computer science, electrical engineering, statistics, systems biology, and mathematics. We work with a variety of data such as microelectronic images and schematics, signal data, text corpuses, ontologies, and networks. Interest in cybersecurity is a must, but experience in cybersecurity is not necessary. No matter where you start from, we value your unique perspective and have a culture that supports continual learning, creativity, and risk taking. As a Data Scientist, you will work on a variety of data-driven problems using cutting edge machine learning, statistics, and data analytics. Many of our projects require developing new methods, and the ideal candidate will be comfortable learning outside of their expertise, conceptualizing a problem, and thinking deeply about data and what it can tell us. We welcome your unique experience in machine learning, statistics, mathematical modeling, data mining and munging, network analysis, natural language processing, or data analytics. However, the most important skills are enthusiasm for learning and data-driven problem solving.

Requirements

A PhD that demonstrates analytical sophistication (Statistics, Mathematics, Physics, Computer Science, Electrical Engineering, Quantitative Social Sciences, Computational Biology or similar) with 1-3 years’ experience, or a master’s degree with 2-5 years’ experience, or a bachelor’s degree with 4-7 years’ experience, or related work experience
Demonstrated experience developing in either Python or R (Python Preferred)
Strong analytical and first principle thinking for solving ambiguous problems with broad definition sets
Experience breaking down and solving complex data problems
Experience researching and applying machine learning, statistics and/or data mining techniques
Excitement about learning new technical subject areas and curiosity about cybersecurity problems
Must be a US Citizen with the ability to obtain and maintain a US government security clearance

Nice To Haves

Experience with deep learning methods, particularly convolutional neural networks (CNN) or computer vision methods
Ability to provide high-level explanations for statistical methods and generate metrics to non-technical experts
Experience with novel data pipelines and working with a variety of data sources
Familiarity with PyTorch or compiled programming languages (such as C++ or C#)
Experience in vulnerability analysis for either software or hardware
Active Secret or Top-Secret government clearance

Responsibilities

Aid in the design and implementation of machine learning and data engineering techniques applied to cyber security problems
Improve existing techniques and devise new techniques to solve security data problems through collaboration with other junior team members
Independently propose and develop creative solutions to unique problems, potentially leading internal research projects
Identify and articulate strengths and weaknesses of solutions conclusions and problem approaches during technical discussions, typically for non-data science audiences
Learn and become fluent in the details of security related domains to devise data driven solutions – this can range from software fuzzing to micro-electronic supply chain analysis