Lead Data Scientist
Lead Data Scientist
Security and Intelligence ServicesThis job is no longer accepting applications
Job Description
The Team :
- As a member of the Commodity Insights you will work on building and deploying ML/LLM powered products and capabilities to power natural language understanding, data extraction, information retrieval and data sourcing solutions for S&P Global Market Intelligence and our clients.
- You will be responsible for the management, deployment and optimization of ML Models and pipelines while leading-by-example in a highly engaging work environment. You will work in a (truly) global team and encouraged for thoughtful risk-taking and self-initiative.
What s in it for you:
- Exciting tasks and the team with own product development in the space of intelligent data solutions based on Machine Learning
Responsibilities:
- Play a central role in all stages of the AI product development life cycle, including:
- Designing Machine Learning systems and model scaling strategies
- Research & Implement ML and Deep learning algorithms for production
- Run necessary ML tests and benchmarks for model validation
- Fine-tune, retrain and scale existing model deployments
- Extend existing ML library s and write packages for reproducing components
- Partner with business leaders, domain experts, and end-users to gain business understanding, data understanding, and collect requirements
- Interpret results and present them to business leaders
- Manage production pipelines for enterprise scale projects
- Perform code reviews & optimization for your projects and team
- Lead and mentor by example, including project scrums
Technical Requirements:
- Proven track record as a ML engineer
- Expert proficiency in Python (Numpy, Pandas, Spacy, Sklearn, Pytorch/TF2, HuggingFace etc.)
- Excellent exposure to large scale model deployment strategies and tools
- Excellent knowledge of ML & Deep Learning domain
- Solid exposure to Information Retrieval, Web scraping and Data Extraction at scale
- Knowledge in AWS Technologies: Terraform, CFT, Lambda, Fargate, ECS and also in SQL, containerization and Django.
- Experience with SOTA models related to NLP like Classification, Summarization, Phrase extraction, Table Extraction and OCR
- Open to learning new technologies and programming languages as required
- Knowledge in model monitoring and retrain & tuning of models
Good to have:
- 4+ years of relevant experience in ML Engineering
- Prior substantial experience regarding document processing
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About Company
Skills:
statistical data analysis , Machine Learning, Natural Language Processing, Jira, Sql, Gitlab, Clustering, Python, UNIX command line, anomaly detection, Classification, data analysis libraries, Agile development frameworks, ML Ops, feature engineering, Azure AI Search
Skills:
data engineering , Machine Learning, Docker, Data Architecture, Python, AWS, Sql, Deep Learning, Gcp, MLops, Rest Apis, Azure, Kubernetes, Pinecone, CI CD, LangGraph, Vector Databases, Chroma, LangChain, CrewAI, Generative AI, AI Security, LLMs, LLMOps, Governance, RAG AI Agents, Milvus, Weaviate, LlamaIndex
Skills:
data engineering , Data Analytics, Web Scraping, Deep Learning, Data Extraction, Nlp, MLops, Azure, Python, AWS, Generative AI, Git-based CI CD workflows, AI Agents, LLMOps, cloud platforms, large-scale data processing, RAG, Data Communication, Multimodal AI, synthetic data techniques, entity recognition
Skills:
Sql, Tensorflow, Pytorch, Python, Predictive Analytics, Cnn, Power Bi, Tableau, Nlp, Rnn, Data Visualization, Clustering, Generative AI, machine learning frameworks, Hugging Face, Digital Marketing Strategies, Classification, deep neural network models, conversational AI solutions, Regression, Transformers, performance optimization, classical analytics tools, NLP models, OpenAI, customer acquisition analytics
Skills:
Databricks, Tensorflow, Github, Pandas, Numpy, Azure Machine Learning, Machine Learning, MLops, Azure Functions, Pytorch, Python, Kubernetes, Docker, XGBoost, Azure DevOps, Azure Monitor, Application Insights, MLflow, scikit-learn, Azure Container Apps, LightGBM