Data Scientist - Entry Level

Company Description

• Collaborate with scientists and researchers in one or more of the following areas: data intensive applications, natural language processing, graph analysis, machine learning, statistical learning, information visualization, low-level data management, data integration, data streaming, scientific data mining, data fusion, massive-scale knowledge fusion using semantic graphs, database technology, programming models for scalable parallel computing, application performance modeling and analysis, scalable tool development, novel architectures (e.g., FPGAs, GPUs and embedded systems), and HPC architecture simulation and evaluation.
• Work with other LLNL scientists and application developers to bring research results to practical use in LLNL programs.
• Assess the requirements for data sciences research from LLNL programs and external government sponsors.
• Carry out development of data analysis algorithms to address program and sponsor data sciences requirements.
• Engage other developers frequently to share relevant knowledge, opinions, and recommendations, working to fulfill deliverables as a team.
• Contribute to technical solutions, participate as a member of a multidisciplinary team to analyze sponsor requirements and designs, and implement software and perform analyses to address these requirements.
• Develop and integrate components-such as web-based user interfaces, access control mechanisms, and commercial indexing products-for creating an operational information and knowledge discovery system.
• Perform other duties as assigned.

• Contribute to multiple parallel tasks and priorities of customers and partners, ensuring deadlines are met.
• Solve abstract problems, converting them into useable algorithms and software modules.
• Provide solutions that require analysis of multiple factors and the creative use of established methods.

• Ability to secure and maintain a U.S. DOE Q-level security clearance which requires U.S. citizenship.
• Bachelor's degree in data science, computer science, mathematics, statistics, or related field, or the equivalent combination of education and related experience.
• Fundamental knowledge of one or more of the following: scientific data analysis, statistical analysis, knowledge discovery, supervised learning, unsupervised learning, deep learning, reinforcement learning, natural language processing, and big data technologies.
• Skilled in all aspects of the data science life cycle: feasibility / background research, data exploration, feature engineering, modeling, visualization, deployment
• Fundamental experience developing data science algorithms with C, Python, or R in Linux, UNIX, Windows environments, sufficient to integrate solutions into larger applications.
• Experience with scikit-learn, PyTorch, TensorFlow, or similar machine learning (AI/ML) development API for the purpose of developing data science solutions.
• Ability to effectively handle concurrent technical tasks with conflicting priorities, to approach difficult problems with enthusiasm and creativity and to change focus when necessary, and to work independently and implement research concepts in a multi-disciplinary team environment, where commitments and deadlines are important to project success.
• Sufficient interpersonal skills necessary to interact with all levels of personnel.
• Sufficient verbal and written communication skills necessary to effectively collaborate in a team environment and present and explain technical information.

• Effective analytical, problem-solving, and decision-making skills to develop creative solutions to complex problems.
• Broad experience with one or more of the following technical languages, concepts, or constructs: Python, scientific data analysis, statistical analysis, knowledge discovery, supervised learning, unsupervised learning, deep learning, reinforcement learning, natural language processing, and big data technologies.
• Proficient experience with at least one of the following advanced ML concepts: Transfer Learning, distributed ML (data/model), ML operations, generative models, Bayesian optimization, computer vision modeling, transformers, graph neural networks, uncertainty quantification, surrogate modeling, or techniques for data-poor ML (low-shot, coresets, etc).

• Flexible Benefits Package
• 401(k)
• Relocation Assistance
• Education Reimbursement Program
• Flexible schedules (depending on project needs)
• Inclusion, Diversity, Equity and Accountability (IDEA) - visit https://www.llnl.gov/diversity
• Our core beliefs - visit https://www.llnl.gov/diversity/our-values
• Employee engagement - visit https://www.llnl.gov/diversity/employee-engagement