Senior Data Scientist

Duties and Responsibilities

Strategic Leadership in Data Science & AI projects

Define and execute the data science strategy to align with organizational goals and departmental objectives.

Collaborate with the CIO, Global Director of MRV and Customer Data, the Data and Analytics Manager, and departmental heads to prioritize data science initiatives for maximum impact.

Establish annual, semi-annual, and quarterly roadmaps for AI & data science, ensuring alignment with organizational priorities.

Serve as the primary advocate for data science across the organization, promoting the value of AI/ML solutions.

AI & Machine Learning Solution Development and Deployment

Ideation and Strategy: Partner with department heads to identify opportunities where AI/ML can create business value, proposing innovative solutions that address organizational challenges.

Model Development: Design, develop, and fine-tune machine learning models and algorithms to support a range of use cases, leveraging advanced techniques as appropriate.

Proof-of-Concept (POC): Lead the creation of POC projects to validate AI/ML approaches, iterating quickly to assess feasibility and impact.

Deployment: Collaborate with Data Engineering and IT to deploy machine learning models into production, ensuring scalability, reliability, and seamless integration.

Optimization: Monitor deployed models for performance, updating and refining them to meet evolving business needs

Collaboration and Stakeholder Engagement

Work with department heads to align data science initiatives with their goals and objectives, ensuring AI/ML solutions drive positive impact.

Partner with the Data Engineering team to ensure the data infrastructure supports AI/ML workloads and adheres to best practices for scalability and reliability.

Ensure clear and consistent stakeholder communication throughout project lifecycle—from ideation and development to deployment and impact assessment—to ensure alignment, transparency, and adoption.

Act as a key liaison between data science technicalities and business units, translating complex concepts into actionable insights for stakeholders.

Data Quality and Governance

Serve as the Data Quality and Governance Champion by leading initiatives to enhance the integrity, reliability, and accessibility of the organization’s data. Focus on improving both processes and outputs for all company datasets by:

Establishing and maintaining robust data quality standards across the organization.

Collaborating with data engineers, analysts, and stakeholders to identify critical data points and implement robust validation and monitoring mechanisms.

Industries:Oil & Energy

Function: Data Science