We are looking forSenior Software Architect (Data Scientist AI Solutions for Electrification),
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.
