Data Governance Senior Specialist

About the Team The Data Science team within the Data Office at CIC is at the heart of the digital transformation of the global SOCAR Group. We are a team of problem solvers with diverse backgrounds spanning fields such as chemical engineering, geosciences, finance, and software development. We operate at the intersection of machine learning, optimisation, and domain expertise, tackling complex industrial challenges that sit across these disciplines. The solutions we develop become part of day-to-day operations, directly impacting operational efficiency, decision-making, and performance across upstream, downstream, and corporate functions. Our team collaborates closely with Data Engineering, Data Analytics, and Data Governance teams to ensure our solutions are production-ready and sustainably delivered. Our mission is to build reliable, scalable, and interpretable AI products that make the energy industry more efficient, data-driven, and intelligent. About the Role We are hiring Machine Learning Specialists at junior, mid, and senior levels to join our growing team. You will work within the Machine Learning section of the Data Science Department, developing and deploying production-grade machine learning and optimisation. Your work will span the full lifecycle from problem definition and data exploration to model development, deployment, continuous improvement, and product development, following established MLOps practices and leveraging internal ML platforms. You will focus on a diverse set of use cases, including traditional machine learning (e.g., forecasting, classification) and optimisation (e.g., linear programming, heuristics). These solutions support critical operational and business decisions across multiple domains, including finance, procurement, petroleum engineering, geosciences, and industrial processes. As a Machine Learning Specialist, you will play a hands-on role in building scalable AI products that deliver real operational impact. Your responsibilities will include: Developing machine learning and optimisation solutions that drive business value Exploring data, engineering features, and extracting insights to support decision-making Designing scalable data pipelines and production-ready model architectures Deploying, monitoring, and maintaining models across their full lifecycle Partnering with engineers, domain experts, and stakeholders to solve complex problems Turning business requirements into practical and reliable AI systems Clearly communicating results and recommendations to technical and business audiences Supporting experimentation, research initiatives, and proof-of-concept development Engaging in workshops to uncover new use cases and product opportunities Contributing to the evolution of MLOps, governance, and ML best practices You will take ownership of problems end-to-end, working in an environment that values experimentation, iteration, and practical impact. Depending on your experience level, you may also play a key role in shaping solutions, guiding stakeholders, changing culture, and contributing to how machine learning products are built and scaled across the organization. What We’re Looking For We’re looking for curious, motivated machine learning practitioners who want to grow, build real systems, and make an impact in a complex industrial environment. Your Background A Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Statistics, or another quantitative field (We also welcome candidates with backgrounds in Geosciences, Chemical/Industrial Engineering, Finance, or petroleum-related fields with strong analytical skills.) Hands-on experience working on data science or machine learning projects — through work, internships, research, or personal projects Exposure to the end-to-end ML lifecycle, from data exploration to model development and evaluation Fluency in Azerbaijani and English (Russian and/or Turkish is a plus) Technical Skills Strong programming skills in Python, with experience using SQL and relational databases Solid understanding of statistics and classical machine learning methods Ability to write clean, maintainable, production-ready code Experience working with Git or similar version control systems Familiarity with modern ML tooling such as: Experiment tracking (e.g., MLflow) CI/CD concepts for data or ML workflows Containerisation (Docker) and basic Linux environments Experience with Kubernetes, GPUs, or cloud platforms is a plus but not required ML & Analytics Exposure (Nice to Have) Applied experience with: Forecasting, classification, or regression problems Deep learning or computer vision models (nice to have, not mandatory) Optimisation or operational research methods (e.g., linear or nonlinear programming) Comfortable working with real-world, imperfect data Professional & Personal Skills Strong analytical thinking and a problem-solving mindset Ability to explain technical results in a clear and practical way Confidence working with cross-functional teams and non-technical stakeholders Good communication and presentation skills Ability to manage multiple tasks while working independently Domain Interest (Plus) Experience or strong interest in applying data and machine learning to real business and industrial problems, especially in: Upstream: geology, geophysics, drilling, reservoir or petroleum engineering Downstream: chemical engineering, process engineering, refinery operations Corporate: finance, procurement, legal, or HR analytics We Offer 5/2, 09.00-18.00; Meal allowance; Annual performance bonuses; Corporate health program: VIP voluntary insurance and special discounts for gyms; Access to Digital Learning Platforms. Interested candidates can apply via the link in the Apply for job button. Note: Only candidates who meet the requirements of the vacancy will be contacted for the next stage.