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

• 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.
• 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.
• 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.
• 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.
• 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.
• 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