S&P Global
Energy
DataScientistIII
“Data Scientist III at S&P Global. Skills: Python, Machine Learning, Cloud Platforms, Computer Vision. Understand data needs. Translate needs into requirements”
What You'll Achieve.
deliver scalable, reliable, cloud-enabled data solutions; shape data infrastructure; influence business decisions; support product innovation; integrate models into production pipelines; use trusted, well-modeled, well-delivered data; build analytics and ML capabilities; support product innovation; support decision-making; create long-term, sustainable value; make superior decisions; make confident decisions; make an impact on the world
Industry & Context.
troubleshoot issues; model performance; model robustness; model maintainability
What They're Looking For.
Must Have
University degree in Computer Science, Engineering, Mathematics, or a related discipline (or equivalent practical experience), 5+ years of hands-on programming experience, Python, machine learning and deep learning models, machine learning frameworks and libraries, PyTorch, TensorFlow, Keras, scikit-learn, neural network architectures, computer vision techniques, image/video processing, feature extraction, object detection, segmentation, representation learning, end-to-end ML workflows, model performance, model robustness, model maintainability, data storage and querying technologies, SQL, NoSQL, cloud platforms, AWS, Azure, GCP, machine learning experimentation, training at scale, model deployment, distributed and accelerated computing concepts, multi-GPU training, distributed training, large-scale inference, data engineers, platform teams, model logic, model performance, version control systems, Git, collaborative ML development, communicate results, trade-offs, limitations of ML models, technical stakeholders, non-technical stakeholders
Nice to Have
workflow orchestration or experiment management tools, Airflow, MLflow, data governance practices, privacy, security, access controls, compliance expectations, data observability, monitoring, alerting, SLAs, incident response
What You'll Do.
Understand data needs
Translate needs into requirements
Design data pipelines
Maintain data pipelines
Develop data infrastructure
Maintain data infrastructure
Improve data reliability
Troubleshoot data issues
Support ML solution deployment
Support ML solution monitoring
Contribute to Agile team
How You'll Work.
Team & Collaboration
Collaborate with stakeholders; Work closely with colleagues; Work with data scientists; Work with analysts; Work with engineers; Work with business teams; Collaborate with data engineers; Collaborate with platform teams; Work closely with data science peers; Work closely with engineering peers; Collaborate in partnership
Communication Scope
communicate results; communicate trade-offs; communicate limitations of ML models; communicate clearly to technical stakeholders; communicate clearly to non-technical stakeholders
Process & Methodology
Agile team environment
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