Data Scientist

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.