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About the role
BURN is looking for a Senior Data Scientist who will spearhead the design, implementation, and scaling of AI and machine learning solutions, driving innovation, and delivering measurable impact across the organization. This role integrates strategic vision, hands-on technical expertise, and cross-functional collaboration to solve business problems, unlock new opportunities, and build a data science function that supports the organization’s long-term goals.
The position will work closely with the Global Director of MRV and Customer Data, department heads, the Data and Analytics Manager, CIO, and IT team to align data science initiatives with organizational objectives. The Senior Data Scientist will also champion data quality and governance, ensuring reliable, accessible, and high-integrity data to support decision-making and AI/ML initiatives. Additionally, the Senior Data Scientist will act as a self-service analytics advocate, driving the adoption and optimization of tools that empower teams to independently access and analyze data, enabling faster and more informed decision-making across the organization.
This is a company-wide role, engaging teams across all departments, and is not confined to the Data and Analytics department.
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.
Addressing gaps in data collection, ingestion, and transformation processes to ensure consistency and accuracy.
Advocating for data governance best practices, aligning them with business objectives, and ensuring adherence to governance frameworks.
Continuously driving improvements through regular audits, automated checks, and stakeholder training to embed a culture of data quality across all departments.
Innovation and Experimentation
Stay informed about advancements in AI/ML and propose experiments to test emerging technologies and methodologies.
Create a sandbox environment for experimentation, encouraging innovation while mitigating risks.
Lead advanced statistical projects, such as A/B testing and hypothesis-driven analysis, to support organizational decision-making.
Self-Service Analytics and Existing Solutions
Champion the organization’s self-service analytics platforms, collaborating with IT and Data Engineering to optimize performance and accessibility.
Lead initiatives to enhance existing AI/ML solutions while developing frameworks for building and scaling new ones.
Ensure all deployed AI/ML solutions align with organizational objectives and contribute to efficiency in decision-making processes.
Skills and Experience
5+ years of experience in data analytics, data science, data management, or business intelligence
Proven experience in developing, deploying, and managing AI/ML solutions in production environments, with a focus on computer vision and agentic AI applications
Strong programming skills in Python, R, or similar languages, with proficiency in ML libraries and frameworks (e.g., Scikit-learn, OpenCV, anomaly detection libraries etc.).
Expertise in data preprocessing, feature engineering, and model evaluation techniques.
Familiarity with cloud platforms (e.g., AWS, Azure) and containerization tools (e.g., Docker, Kubernetes).
Solid understanding of data quality, governance, and infrastructure best practices.
Bachelor’s or Master’s degree in data science, Computer Science, Statistics, or a related field.
