Data Governance & Portfolio Enablement Specialist_GSO

Position Summary: The Data Governance and Portfolio Enablement Specialist is responsible for analyzing complex datasets, developing predictive models, and generating actionable insights that support strategic decision-making. You must safeguard data governance across service business units and support Portfolio Management, so every initiative becomes a governed use case built on governed & owned data. Ideal candidates shall have deep data-governance expertise (ownership, quality, classification), fluent portfolio & use-case management and strong cross-functional facilitation experience. Further you should be comfortable with modern data-product thinking — reusable, standardized data products & artifacts (datasets, models, pipelines) enriched with metadata, data contracts, quality rules, governance policies & SBOM, with ownership aligned to a domain or use case. A Snapshot of your Day How You’ll Make an Impact (responsibilities of role) Strategic • Define and maintain data governance standards, policies, and operational procedures. • Establish data ownership, stewardship, classification, and quality controls. • Support digital portfolio intake, prioritization, and use-case tracking processes. • Enable creation and reuse of governed data products across business domains. • Collaborate with business, IT, legal, compliance, and cybersecurity partners. • Drive metadata management, lineage, and data quality initiatives. • Enforce governed data approaches — sources, contracts, reuse • Settle data ownership early with domain & business owners • Surface reusable, governed data products across service business units Operational 1. Data Analysis & Insights • Collect, explore, and analyze large datasets using statistical methods. • Identify trends, correlations, and actionable insights to support business decisions. • Communicate findings through clear visualizations, dashboards, and reports. 2. Predictive Modeling & Machine Learning • Develop, train, and validate predictive models for classification, regression, clustering, time-series forecasting, or recommendation systems. • Perform feature engineering, feature selection, and model optimization. • Employ ML frameworks such as Scikit-learn, TensorFlow, PyTorch, or XGBoost. 3. Data Pipeline Development • Build and maintain data preprocessing and transformation pipelines. • Work with data engineers to ensure reliable data availability and quality. • Write clean, efficient code in Python or R for modeling and analysis logic. 4. Experimentation & Statistical Testing • Design and run A/B tests or experimental studies. • Apply statistical methods to validate hypotheses and measure impact. • Ensure the integrity and rigor of analytical methodologies. 5. Collaboration & Business Integration • Work closely with product managers, engineers, domain experts, and leadership teams. • Translate complex analytical results into actionable recommendations. • Support product development through data-driven insights and modeling. 6. Research & Continuous Improvement • Stay updated with emerging trends in ML, AI, data analytics, and tools. • Experiment with new algorithms, technologies, and approaches. • Contribute to improving internal data science frameworks and practices. What You Bring (required qualification and skill sets) • Bachelor’s or master’s degree in data science, Computer Science, Statistics, Mathematics, Engineering, or related field. • 5-8+ years of experience in data science or applied analytics. • Experience delivering enterprise-scale digital solutions • Strong communication and stakeholder management skills • Experience working in global cross-functional teams • Data Governance Frameworks, Data Catalog Solutions, Metadata Management, Data Lineage, Snowflake, Power BI • Strong experience with Python or R and data libraries (Pandas, NumPy, SciPy). • Proficiency in ML frameworks (Scikit-learn, TensorFlow, PyTorch). • Good understanding of statistical modeling, hypothesis testing, and experimental design. • Experience working with SQL and cloud data platforms (AWS, Azure, GCP). • Curious, detail-oriented, and passionate about data-driven solutions. • Ability to explain technical results to non-technical stakeholders. Preferred Qualifications • Experience with big data tools (Spark, Databricks, Kafka, Hadoop). • Familiarity with MLOps platforms (MLflow, Kubeflow, DVC). • Knowledge of data visualization tools (Tableau, Power BI, Plotly). • Background in time-series forecasting, NLP, or computer vision. • Experience in domain-specific analytics (finance, IoT, geospatial, utility networks, etc.). • Working experience with Snowflake, Power BI, Microsoft Fabric, Collibra