Data Scientist

Data Scientist

Oakland, CA, US, 94612 (REMOTE)

Direct Hire

**The role is hybrid working from your remote office and in-person based on business needs or company requirements**

Description:

• As part of the centralized Hub team, support data science, AI/ML/GenAI and advanced analytics Spokes across the company by spearheading the implementation of best practices in the development of AI/ML/GenAI and other advanced data science and analytics models, in aspects such as code engineering and best practices in coding, statistical and probabilistic problem modeling, product scalability, AI/ML/GenAI model evaluation, and the like.
• Support the design, implementation, and continuous improvement of governance tools (policies, standards, and processes) for the effective and safe development of AI/ML/GenAI and data science models as a product. Continuously educate Spokes on governance requirements. Monitor compliance and escalate as needed.
• Development of governance documents (policy, standards, and processes) for emerging and disruptive technologies such as Generative AI, Foundational Models, automation, and hyper-automation technologies, etc. Keeping abreast of existing AI and other emerging technology regulations at the state and national level to pivot internal compliance.
• Develop and maintain an emerging technologies evaluation toolkit to assess technology maturity level and its readiness for value realization of business goals. Current focus of said toolkit revolves around Generative AI and automated decision making by AI algorithms, monitoring elements such as model hallucinations and misinformation, training biases, etc.
• Advise and consult delivery teams in the optimal implementation of advanced technologies as proof of concept, balancing risk, and innovation to accomplish business goals.
• Support the identification and implementation of process improvement at the department (EDS&AI) level. Support the adoption of Lean methodologies across EDS&AI.
• Support building data science capability by advising Hub teams as well as Spokes across the enterprise, with the end goal to contribute to improved decision-making in all functional areas.
• Present findings and make recommendations to officers and cross-functional management.
• Educate the internal community (including Executives) on emerging trends. Continuously monitor new technologies and assess their impact and potential disruption to business programs.
• Effectively communicate a compelling vision of data science and AI/ML/GenAI technologies that add value to the company.

Qualifications

• Minimum Education:
• Bachelor’s Degree in Data Science, Machine Learning, Computer Science, Physics, Econometrics or Economics, Engineering, Mathematics, Applied Sciences, Statistics, or equivalent field.

Desired Education:

• Doctoral Degree or higher in Data Science, Machine Learning, Computer Science, Physics, Econometrics or Economics, Engineering, Mathematics, Applied Sciences, Statistics, or equivalent field.

Minimum Work Experience:

• 6 years in data science (or no experience, if possess Doctoral Degree or higher, as described above).

Desired Work Experience:

• Relevant industry (electric or gas utility, renewable energy, analytics consulting, etc.) experience

Knowledge, Skills, Abilities and (Technical) Competencies:

• Active participation in the external data science/artificial intelligence/machine learning community of practice, as demonstrated through volunteering in professional organizations for the advancement of the field, presentations in conferences or publications to disseminate data science knowledge and topics, or similar activities.
• Competency with data science standards and processes (model evaluation, optimization, feature engineering, etc.) along with best practices to implement them.
• Knowledge of industry trends and current issues in job-related area of responsibility as demonstrated through peer reviewed journal publications, conference presentations, open source contributions or similar activities.
• Competency with commonly used data science and/or operations research programming languages, packages, and tools for building data science/machine learning models and algorithms.
• Proficiency in explaining in breadth and depth technical concepts including but not limited to statistical inference, machine learning algorithms, software engineering, model deployment pipelines.
• Mastery in clearly communicating complex technical details and insights to colleagues and stakeholders.
• Mastery of the mathematical and statistical fields that underpin data science.
• Ability to develop, coach, teach and/or mentor others to meet both their career goals and the organization goals.