Siemens Data Scientist – AI Solutions Electrification 

India, Karnataka, Bengaluru 

742631032

You’ll make an impact by:

• Lead the development of Machine Learning (ML), Deep Learning, and Reinforcement Learning solutions from experimentation to production.
• Identify and frame high-impact opportunities for AI/ML across product workflows and operational processes.
• Perform rapid PoCs and feasibility studies to validate new ideas using real-world datasets.
• Define success metrics and develop robust evaluation strategies to measure the effectiveness and accuracy of ML models.
• Collaborate with product managers, software developers, data engineers, and cloud architects to integrate ML models into scalable systems.
• Select appropriate infrastructure based on performance, cost, and latency constraints, ensuring support for online learning, batch inference, and A/B testing.
• Contribute to data strategy, feature engineering, and feedback loops to continuously improve model performance.
• Ensure responsible AI practices, including model transparency, fairness, explainability, and observability.

Use your skills to move the world forward!

• Master’s or Bachelor’s degree in Data Science, Computer Science, Statistics, or a related quantitative field.
• 10+ years of software engineering experience with minimum 5 years of experience in applying ML and DL to solve real-world problems.
• Strong expertise in supervised, unsupervised, and reinforcement learning techniques.
• Experience building and deploying models using frameworks like scikit-learn, TensorFlow PyTorch, or XGBoost.
• Proficient in Python and data manipulation libraries such as Pandas, NumPy, and SQL.
• Familiarity with cloud-based AI workflows using AWS (Sagemaker, Bedrock) and/or Azure (ML Studio, OpenAI Service).
• Solid understanding of evaluation metrics, bias/variance tradeo_¯s, and tuning strategies.
• Hands-on experience with scalable data pipelines, streaming data processing, and distributed training setups.
• Awareness of observability and monitoring practices for models in production.
• Experience working with business stakeholders to translate domain problems into data science solutions.
• Previous experience in the Power, Energy, or Electrification sectors is a strong advantage.
• Experience with experiment tracking tools like MLflow or Weights & Biases, and model versioning systems.