Data Scientist – AI Solutions for Electrification

We are looking for: Senior Software Architect – (Data Scientist – AI Solutions for Electrification),

You’ll make an impact by:

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

Use your skills to move the world forward!

1. Master’s or Bachelor’s degree in Data Science, Computer Science, Statistics, or a related quantitative field.
2. 10+ years of software engineering experience with minimum 5 years of experience in applying ML and DL to solve real-world problems.
3. Strong expertise in supervised, unsupervised, and reinforcement learning techniques.
4. Experience building and deploying models using frameworks like scikit-learn, TensorFlow PyTorch, or XGBoost.
5. Proficient in Python and data manipulation libraries such as Pandas, NumPy, and SQL.
6. Familiarity with cloud-based AI workflows using AWS (Sagemaker, Bedrock) and/or Azure (ML Studio, OpenAI Service).
7. Solid understanding of evaluation metrics, bias/variance tradeo_¯s, and tuning strategies.
8. Hands-on experience with scalable data pipelines, streaming data processing, and distributed training setups.
9. Awareness of observability and monitoring practices for models in production.
10. Experience working with business stakeholders to translate domain problems into data science solutions.
11. Previous experience in the Power, Energy, or Electrification sectors is a strong advantage.
12. Experience with experiment tracking tools like MLflow or Weights & Biases, and model versioning systems.