Job description
s&p enterprise data organization
senior ml engineer
job description
the team
: as a member of the data transformation - cognitive engineering team you will work on building and deploying ml 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 spearhead deployment of ai products 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:
be a part of a global company and build solutions at enterprise scale lead a highly skilled and technically strong team (including leadership) contribute to solving high complexity, high impact problems build production ready pipelines from ideation to deployment
responsibilities:
* design, develop and deploy ml powered products and pipelines
* mentor a team of senior and junior data scientists / ml engineers in delivering large scale projects
* 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 senior / lead 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
* exposure to the following technologies - r-shiny/dash/streamlit, sql, airflow, redis, celery, flask/django/fastapi, scrapy
* experience with sota models related to nlp and expertise in text matching techniques, including sentence transformers, word embeddings, and similarity measures
* open to learning new technologies and programming languages as required
* a master's / phd from a recognized institute in a relevant specialization
good to have:
* 5+ years of relevant experience in ml engineering
* prior substantial experience from the economics/financial industry
* prior work to show on github, kaggle, stackoverflow etc.