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