LifeScore Labs - Lead Data Scientist
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LifeScore Labs - Lead Data Scientist
Essential Responsibilities
- Set strategy and assume a leadership role in a domain of expertise within agile, startup environment
- Develop roadmaps for projects, services, data, and technology
- Oversee operations of algorithm and system deployments
- Provide domain expertise within new business development activities
- Partner with leadership and broader Data Science team to ensure alignment of data science initiatives
- Lead projects and research initiatives
- Develop algorithms and predictive models, create prototype systems, visualizations, and web applications
- Design and analyze experiments
- Assemble data sets from disparate sources and analyze using appropriate quantitative methodologies, computational frameworks and systems
- Disseminate findings to non-technical audiences through a variety of media, including interactive visualizations, reports and presentations
- Mentor junior team members
Desired Skills and Experience
- Industry recognized expertise
- 7+ years working with data and relevant computational frameworks and systems
- 7+ years developing of probabilistic models and machine learning algorithms
- Proficient level of understanding in the following areas and an expert in at least one: machine learning, probability and statistics (esp. Bayesian methods), natural language processing, operations research
- Exceptional problem-solving skills and willingness to learn new concepts, methods, and technologies
- Expert in data analysis using R or Python (numpy, scipy, matplotlib, scikit-learn, pandas, etc.) programming languages
- Knowledge of HTML/CSS/Javascript, d3.js and web application frameworks
- Knowledge of software development and engineering best practices
- Knowledge of automation, cloud computing platforms and technology (AWS, kubernetes, Jenkins, containers)
- Experience in database design and SQL
- Ability to work in a highly agile and collaborative environment
- Outstanding presentation and communication skills (publication history a plus)
Education
M.S. or Ph.D. in a quantitative discipline (Computer Science, Statistics, Applied Mathematics, Electrical Engineering, Physics, etc.) is required
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