About
Pay Rate: $80-$110 per hour Duration: 12 Months Location: San Ramon, CA Work Mode: Hybrid Responsibilities
Lead the development of high-complexity models and training sets. Provide hands-on execution and implementation of data science models. Translate business analysis needs into well-defined data science problems. Select appropriate models and algorithms and communicate model evaluation and implications of results back to stakeholders. Recognize and prioritize the most important work related to data science models to achieve the highest operational impact for analytics in the business. Balance trade-offs among analytics value, model development methods, and design and technologies used to implement data science models with a bias toward action. Perform collaborative work on data science problems and mentor junior data scientists. Create shared process models, business objects, activity diagrams, and process documentation to effectively articulate multiple views of the business solutions that support technical architecture. Manage the development of quantitative models and tools. Collaborate with leaders, other LOBs, and business partners to work on issues, projects, or activities. Develop new or revise complex models to predict business demand trends, and volume and expenditures forecasts capacity analysis, and various other metrics to identify potential opportunities. Assess business implications associated with modeling assumptions, inputs, methodologies, technical implementation, analytic procedures, and processes, and advanced data analysis. Partner with leaders to drive high performance in their lines of business. Develop a deep understanding of business drivers and financial levers to provide strategic decision support. Oversee the resolution of complex projects and programs. Develop and maintain up-to-date detailed project schedules and work plans. Perform analysis on complex data models requiring customized reports and data and present recommendations. Requirements
Bachelor's Degree in Econometrics, Economics, Engineering, Mathematics, Applied Sciences, Statistics, or job-related discipline or equivalent experience. Minimum 8 years of job-related experience, or a Master's Degree with 6 years of job-related experience, or a Doctorate Degree with 3 years of job-related experience. Experience in data modeling, minimum 5 years. Hands-on experience building and deploying ML models for real-world applications. Strong command of languages like Python, R, and SQL for data manipulation and model development. Solid foundation in probability, linear algebra, calculus, and statistical inference. Provide insights in a clear and concise manner for upper management review through various mediums such as graphics, PowerPoint presentations, dashboards, etc. Preferred Skills
PhD in engineering or a related field (computer science, natural sciences, mathematics). Experience with Python, R, Scala, SQL. Experience developing solutions with Pandas/Scikit-learn, Spark, or comparable technologies. Experience with data science notebooks (Jupyter, Zeppelin, or other). Experience with AWS, Azure, cloud computing technologies. Scrum team experience. Energy industry experience. Experience designing efficient data science workflows and database architecture for data science purposes. Experience with forecasting, Bayesian networks, and graph analytics. Strong statistics experience. Experience with software development methodologies and software engineering principles. Knowledge of program management theories, concepts, methods, best practices, and techniques as needed to perform at the job level. Knowledge of relevant programming languages - for example, Visual Basic, Ladder Logic, Programmable Logic Controller, C, SharePoint, HTML, Java, Adobe - as needed to perform at the job level. Competency in knowing the most effective and efficient processes to get things done, with a focus on continuous improvement. Knowledge of principles, techniques, and procedures used for the production and design of technology-based equipment and systems as needed to perform at the job level. Knowledge of statistical theories, concepts, methods, best practices, and analyses as needed to perform at the job level. Ability to develop reports, models, and simulations as needed to perform at the job level. Competency in developing and delivering multi-mode communications that convey a clear understanding of the unique needs of different audiences. Knowledge of data model design philosophies and methodologies for data warehouse and OLTP systems.
Languages
- English
Notice for Users
This job comes from a TieTalent partner platform. Click "Apply Now" to submit your application directly on their site.