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 up-to-date 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 explicit 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).
- Positive 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

📌 Data Governance & Portfolio Enablement Specialist_GSO (Gurugram)
🏢 Siemens
📍 Gurugram