Position Summary: 

The Data Scientist is responsible for analyzing complex datasets, developing predictive models, and generating actionable insights that support strategic decision-making. This role combines statistical analysis, machine learning, data engineering, and domain expertise, including the application of advanced AI techniques such as large language models (LLMs) to uncover patterns, forecast outcomes, and drive data-driven products and business innovations. The Data Scientist leverages telemetric data and weather-based modelling and computation to enhance predictive accuracy and support operational and strategic initiatives in the energy sector. Collaborating with cross-functional teams, the Data Scientist translates business challenges into analytical solutions, employing LLMs for natural language understanding and generation, and integrates telemetry and meteorological data to deliver measurable impact and support Siemens Energy’s commitment to sustainable and reliable energy transformation.

A Snapshot of your Day

How You’ll Make an Impact (responsibilities of role)

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.
• 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).
• Strong analytical and problem-solving abilities.
• Curious, detail-oriented, and passionate about data-driven solutions.
• Excellent communication and storytelling skills.
• Ability to explain technical results to non-technical stakeholders.
• Collaborative mindset with strong ownership and accountability.

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.).