Job Description

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