Careers - Principal Data Scientist - Power and Renewables

Position: Enverus Careers - Principal Data Scientist - Power and Renewables- 26205
Principal Data Scientist - Power and Renewables

Why YOU want this position

At Enverus, we're committed to empowering the global quality of life by helping our customers make energy affordable and accessible to the world.

We are the most trusted energy-dedicated SaaS company, with a platform built to maximize value from generative AI, and our innovative solutions are reshaping the way energy is consumed and managed. By offering anytime, anywhere access to analytics and insights, we're helping our customers make better decisions that help provide communities around the world with clean, affordable energy.

The energy industry is changing fast. But we've continued to lead the way in energy technology, creating intelligent connections across the entire energy ecosystem, from renewables, power and utilities to oil and gas and financial institutions. Our solutions create more efficient production and distribution, capital allocation, renewable energy development, investment and sourcing, and help reduce costs by automating crucial business operations.

Of course, this wouldn't be possible without our people, which is why we have built a team of individuals from a diverse range of backgrounds.

Are you ready to help power the global quality of life? Join Enverus, and be a part of creating a brighter, more sustainable tomorrow.

We are currently seeking a Principal Data Scientist for Enverus Power and Renewables, who will create measurable customer value. You will partner closely with Product Management and Engineering to define and execute short- and long-term data roadmaps, applying advanced statistical, machine learning, and data engineering techniques across multiple product areas.

Performance Objectives

1. Strategic Data Analysis:
Lead strategic data analysis initiatives, leveraging advanced statistical techniques, predictive modeling, and machine learning algorithms to uncover valuable insights. Analyze complex data sets to identify patterns, trends, and correlations that inform product strategy, optimization, and innovation.

2. Product Strategy Alignment:
Collaborate closely with product managers, executives, and stakeholders to align data analysis efforts with the overall product strategy. Provide strategic guidance and insights to drive product roadmap prioritization, feature development, and market positioning.

3. Business Impact Assessment:
Assess the impact of product initiatives on key performance metrics, customer satisfaction, and business outcomes. Conduct rigorous analysis to measure the effectiveness of product changes, identify causal relationships, and provide recommendations for continuous improvement.

4. Data Governance and

Infrastructure: Oversee data governance processes, ensuring data integrity, security, and compliance. Collaborate with data engineering and IT teams to optimize data collection, storage, and retrieval processes. Drive improvements in data infrastructure and data management practices.

5. Thought Leadership and

Innovation: Stay at the forefront of data analysis methodologies, tools, and industry trends. Champion innovative approaches to data analysis, experiment design, and modeling techniques. Act as a thought leader in the field of data analytics, influencing the organization's data-driven culture.

6. Stakeholder

Collaboration:

Collaborate closely with cross-functional teams, including product managers, engineers, data scientists, and executives, to provide insights and recommendations. Influence decision-making processes by effectively communicating complex data insights and demonstrating the value of data-driven strategies.

7. Team Leadership and Mentorship:
Provide leadership and mentorship to the data analytics team. Foster a culture of continuous learning, collaboration, and growth. Guide and mentor junior analysts, helping them develop their analytical skills and business acumen.

Competitive Candidate Profile

* Advanced Data Science:
Expertise in advanced data analysis techniques, including statistical modeling, predictive analytics, and machine learning. Proficiency in programming languages such as Python, and strong SQL skills for data extraction and manipulation.

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