Data Scientist

2 weeks ago

Oceanside, United States Avispa Full time

Data Scientist ROCGJP00025159

A leading biotechnology company is seeking a Data Scientist. The successful candidate will be key to actively managing the ML(machine learning) model through its lifecycle and working closely with Subject matter experts within the manufacturing, quality and development organizations of PT to deliver fit-for-purpose analytics. The ideal candidate has extensive knowledge of ML frameworks, data structures, modeling, and software architecture.

Data Scientist Pay And Benefits

Hourly pay: $45-$55/hr Worksite: Leading biotechnology company (Oceanside, CA 92056, Hybrid with 2-3 days a week in the office) W2 Employment, Group Medical, Dental, Vision, Life, Retirement Savings Program, PSL 40 hours/week, 6 Month Assignment

Data Scientist Responsibilities

Be part of a cross-functional use case team to address high-pressing business problems and become familiar with our product-related data, manufacturing processes and systems. Undertake appropriate preprocessing of structured and unstructured data with additional support of Data Engineers and potential collaborate in building new Data Products. Analyze small to large amounts of static and real-time data to discover trends and patterns, and to implement analysis pipelines. Build, validate, and deploy ML models and algorithms to predict manufacturing-related issues of the products. Maintain the ML model lifecycle to ensure it fits for the intended use over time. Be able to work as work as an innovator, inventor, investigator, partner, engineer, “firefighter'' and, of course, as a scientist. Stay hands-on with implementation data and analytics pipelines, and build new business solutions and challenge the status quo.

Data Scientist Qualifications

2+ years of relevant work experience. Master’s with 2 years relevant work experience, or PhD degree in Software Engineering, Statistics, Data Science, Bioinformatics or a related field. Rich experience in ML model validation and lifecycle management. Advanced knowledge of programming languages (Python, R) and ability to implement pipelines. Extensive knowledge of ML frameworks, data structures, modeling, and software architecture. Familiar with code optimization. In-depth knowledge of applying unsupervised and supervised models and ability to identify linear and non-linear relationships in data. Proficiency in developing data and model pipelines and using cloud computing to run pipelines and ML models (GCP/AWS). Superb analytical and problem-solving abilities. Ability to communicate a compelling story and complex concepts with aspirational language that data professionals, business stakeholders and decision makers equally understand.

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