Location: Houston, TX 77077 - Onsite 4 days per week.
Overview of Role:
Are you a motivated Machine Learning Engineer looking to join a growing and innovative team?
The ideal candidate will be responsible for developing and fine-tuning AI models, with a focus on text classification and Natural Language Processing (NLP). You should have extensive experience using different ML libraries like NLTK, Tensorflow, and PyTorch. You will work closely with our clients to understand their specific needs, refine existing models, and deploy solutions that address their unique challenges.
Company Description:
Our client manages a large portfolio of companies and has a large focus on upholding integrity and being held accountable to the highest ethical standards. They are committed to fostering a culture of open communication, teamwork, and personal development.
Role Description:
Translate business needs into analytics/reporting requirements for data-driven decisions.
Integrate Gen AI across different business platforms according to needs.
Deploy models on both on-prem and cloud environments (mainly AWS).
Manage use case design and build teams, providing guidance as they develop and operationalize data science models.
Ensure analytical insights and products are integrated into business processes.
Guide and approve analytics/modeling approach, model deployment requirements, and quality assurance standards.
Provide input to the long-term plan for the Machine Learning team, focusing on talent acquisition.
Work within a scrum team across various business units.
Work with business and technical stakeholders to identify new technologies and opportunities that deliver measurable business value.
Skills and Experience:
Master's degree or PhD in Computer Science, Statistics, Applied Mathematics, or related field, with 5-7 years experience in a data science or similar role.
Strong proficiency in Python (preferred) or Javascript.
Extensive knowledge and working experience with Machine Learning libraries/frameworks, and data processing/visualization tools. (PyTorch, Tensorflow, NumPy, etc.)
Strong communication skills with experience presenting to stakeholders and breaking down technical topics to non-technical team members.
Experience with cloud computing environments (AWS, Azure, GCP) and Data/ML platforms (Databricks, Spark).
Experience in Supervised and Unsupervised Machine Learning.
Experience in containerization using orchestration platforms like Docker or Kubernetes.