Data Governance & Portfolio Enablement Specialist_GSO

Siemens; Gurugram District; On-site; INR 2,500,000 - 4,500,000; Full time

Job summary

Siemens seeks a Data Governance and Portfolio Enablement Specialist to analyze complex datasets, build predictive models, and deliver actionable insights for strategic decisions.

You will safeguard data governance across service units, govern data products, and collaborate with product, IT, legal, and cybersecurity teams while driving metadata, lineage, and quality initiatives. Strong Python/R, SQL, Snowflake, and ML skills are essential.

Qualifications

• Bachelor’s or master’s degree in data science, CS, statistics, math, 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.

Responsibilities

• 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.
• Data Analysis & Insights: collect, explore, and analyze large datasets.
• Identify trends, correlations, and actionable insights to support decisions.
• Communicate findings through visualizations, dashboards, and reports.
• Predictive Modeling & ML: develop, train, validate models for classification, regression, forecasting, or recommendations.
• Work with data engineers to ensure data availability and quality.
• Write clean Python or R code for modeling and analysis logic.
• Experimentation & Statistical Testing: design and run A/B tests; validate hypotheses.

Skills

Data governance

Python

SQL

Power BI

Snowflake

Cloud platforms

Education

Bachelor’s or master’s in data science

Relevant engineering/CS/Math degree

Tools

Snowflake

Power BI

Tableau

Databricks

Kafka

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.

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