Avispa Technology
Data Scientist - Machine Learning
Avispa Technology, San Francisco, California, United States, 94199
Data Scientist - Machine Learning ROCGJP00027870A leading biotechnology company is seeking a Data Scientist - Machine Learning. The right candidate will work closely with multi-disciplinary teams to design, develop and deploy structured, high-quality data solutions in particular Large Language Model (LLM) applications. These solutions will be leveraged across the Product Development organization to help our teams fulfill our mission: to do now what patients need next. We focus on delivering technology that evolves the practice of medicine and helps patients live longer, better lives. We are a diverse team of open and friendly people, enthusiastic about technological novelties and optimal enterprise solutions. We share knowledge, experience & appreciate different points of view.Data Scientist - Machine Learning
Pay and Benefits:Hourly pay:
$75-$85/hr
(pay varies based on candidate's experience)Worksite: Leading biotechnology company (South San Francisco, CA 94080)W2 Employment,
Group Medical, Dental, Vision, Life,
Retirement Savings Program,
PSL40 hours/week, 12 Month AssignmentData Scientist - Machine Learning
Responsibilities:Partner with fellow Data Scientists, ML engineers, MLOps / DevOps engineers and cross functional teams to solve complex problems and create unique solutions by using modern NLP technologies in particular LLMs.Build data pipelines and deployment pipelines for ML models.Development of ML models according to business and functional requirements.Able to help deploy various models and tune them for better performance.Document and communicate the design and implementation details.Contribute to the DSE AI team on technical decisions.Collaborate with clients, and informatics departments to deploy scalable and easy-to-maintain solutions.Serves as a technical point of contact for enterprise-wide technologies solutions. Leads complex troubleshooting efforts and root cause analysis.Data Scientist - Machine Learning
Qualifications:2+ years of commercial Data Engineering / ML Engineering / MLOps / UI/UX engineering experience.3+ years of commercial software engineering experience.Master in quantitative field (e.g. mathematics, statistics, computer science, EE, etc.), and/or Life Sciences degree with significant computational experience, or equivalent, with 5+ year working experience in Data Science. PhD a plus.Experience with LLM applications development including tool using and reasoning, for instance RAG solution and code interpreter.Experience with LLM fine tuning a big plus.Experience in building data pipelines and deployment pipelines for LLM applicationsRecent experience with ML/AI toolkits such as AWS Sagemager (other toolkits like Pytorch, Tensorflow, Keras, MXNet, H20, etc are nice to have).Experience with MLOps technologies (Sagemaker, Vertex AI, Kubeflow).Experience with deployment of scalable apps a plus.Experience with clinical study data a plus.Experience with cloud solutions (AWS / Azure / GCP), docker.Proven scripting and automation skills.Good knowledge of: git, bash, linux, CI/CD tools (e.g. jenkins, gitlab CI), software lifecycle, RDB, visualization tools eg Tableau, Jira, confluence.Programming languages: Python, R.Test driven development, good coding practices.Problem-solving and decision-making skills.Good interpersonal skills.Customer & delivery focus.Ability to work effectively with team members and virtual teams from different locations and different cultural backgrounds.
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Pay and Benefits:Hourly pay:
$75-$85/hr
(pay varies based on candidate's experience)Worksite: Leading biotechnology company (South San Francisco, CA 94080)W2 Employment,
Group Medical, Dental, Vision, Life,
Retirement Savings Program,
PSL40 hours/week, 12 Month AssignmentData Scientist - Machine Learning
Responsibilities:Partner with fellow Data Scientists, ML engineers, MLOps / DevOps engineers and cross functional teams to solve complex problems and create unique solutions by using modern NLP technologies in particular LLMs.Build data pipelines and deployment pipelines for ML models.Development of ML models according to business and functional requirements.Able to help deploy various models and tune them for better performance.Document and communicate the design and implementation details.Contribute to the DSE AI team on technical decisions.Collaborate with clients, and informatics departments to deploy scalable and easy-to-maintain solutions.Serves as a technical point of contact for enterprise-wide technologies solutions. Leads complex troubleshooting efforts and root cause analysis.Data Scientist - Machine Learning
Qualifications:2+ years of commercial Data Engineering / ML Engineering / MLOps / UI/UX engineering experience.3+ years of commercial software engineering experience.Master in quantitative field (e.g. mathematics, statistics, computer science, EE, etc.), and/or Life Sciences degree with significant computational experience, or equivalent, with 5+ year working experience in Data Science. PhD a plus.Experience with LLM applications development including tool using and reasoning, for instance RAG solution and code interpreter.Experience with LLM fine tuning a big plus.Experience in building data pipelines and deployment pipelines for LLM applicationsRecent experience with ML/AI toolkits such as AWS Sagemager (other toolkits like Pytorch, Tensorflow, Keras, MXNet, H20, etc are nice to have).Experience with MLOps technologies (Sagemaker, Vertex AI, Kubeflow).Experience with deployment of scalable apps a plus.Experience with clinical study data a plus.Experience with cloud solutions (AWS / Azure / GCP), docker.Proven scripting and automation skills.Good knowledge of: git, bash, linux, CI/CD tools (e.g. jenkins, gitlab CI), software lifecycle, RDB, visualization tools eg Tableau, Jira, confluence.Programming languages: Python, R.Test driven development, good coding practices.Problem-solving and decision-making skills.Good interpersonal skills.Customer & delivery focus.Ability to work effectively with team members and virtual teams from different locations and different cultural backgrounds.
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