Note: The job is a remote job and is open to candidates in USA. QuadSci is an AI product company focused on enhancing customer experiences for software vendors. The Machine Learning Engineer will analyze diverse data sets and develop AI products to improve Go-To-Market performance for customer-centric organizations.
Responsibilities
- Field deployed AI/ML Engineer working across 1 - 5 customers
- Analyzing diverse and dynamic data sets across application telemetry, CRM, Support, Accounting / Billing, Website Analytics and other common enterprise data sources
- Utilize our auto feature engineering assets focused on large telemetry data sets (TBs to PBs)
- Deploy, train, test and monitor AI products at scale within a Customers’ operating architecture in platforms such as Vertex AI
- Engage with other QuadSci deployed colleagues on the explanation of data insights, confirmation of design requirements, root cause analysis, etc
- Work with Customers on AI feature roadmaps (incl. GenAI applications) for their models and ongoing performance management of deployed AI models
- Collaborate with QuadSci colleagues, Customer Cloud Ops and Partners on ML ops, integrations and performance management
- Contribute to QuadSci codebase for auto-feature engineering, AI product packaging, next best action logic and more
Skills
- Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics or other related field
- 3+ years of relevant experience in data science or product development
- Experience with data science and cloud computing environments like VertexAI, Sagemaker or similar tool
- Demonstrated success in the use of clustering, classification, regression, decision trees etc to deliver transformative insights
- Strong experience in building, training, and tuning predictive machine learning, especially in the domains of next best offer or recommended action based on time-series type data sets
- Experience in Natural Language Processing (NLP) for sentiment analysis and behavioral modeling
- Hands on experience with Pandas, Polars and / or Dask (Coiled) for data transformation & feature engineering
- Masters degree in Computer Science, Data Science, Applied Statistics or a related field
- Deployment architecture experience for MLOps optimization using technologies like Dagster or similar tool
- Experience in the use of Large Language Models (LLMs, both Open Source & Proprietary), Embeddings algorithms, Vector DBs and APIs to create GenAI applications
- Familiarity with technologies and/or data architectures such as: Pendo, Salesforce and Open Telemetry
- Direct experience in the use of AI/ML to automate essential business processes that have an impact to Field teams or Customers
Company Overview
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