We are looking to hire a Data Science Lead who can effectively balance rapid execution and delivery with innovative data exploration to serve the business most effectively. You have strong opinions, weakly held, and while well-versed technically and mathematically, know when to choose the right technique, for the right job, at the right level of complexity. You will work closely with our other Data, Product, Engineering, and Design groups to help turn targeted R&D around modeling patient, site, sponsor, and clinical logistics behaviors into solutions that can be used to practical benefit.
- Minimum Qualification:Degree
- Experience Level:Mid level
- Experience Length:4 years
- Exploring rich historical data from sites, sponsors, and other stakeholders and using it to develop models to help our sponsors make more effective study planning, logistics, and and enrollment management decisions as early as possible
- Working with our Data Products division to turn your team’s insights and models into enterprise-ready recommendation systems and other intelligent features for our users
- Ensuring your team develops clean, reusable, and testable code and make use of existing data team libraries and modules.
- Supporting ways to make protocol feasibility assessments more efficient for novel studies
- Becoming intimately familiar with HIPAA, GDPR, and other applicable regulatory and privacy frameworks and how they influence our analytical and model development decisions
- Regularly communicating your efforts to the Director of Analytics, Head of Data, and other technical/non-technical stakeholders in clear written, verbal, or presentation form
- Living our data philosophy, which focuses on ethical decision making, being aware of how biased data (and assumptions) can affect results (and, more importantly, people), and being laser-focused on business needs
- Minimum of 4 years in a data science-related leadership role with a successful track record of team execution at a growing company, ideally in a space similar to ours.
- Demonstrated experience in Agile methodologies and ability to translate business goals into discrete, tactical execution plans while adapting to evolving technical and business constraints.
- Expertise in several techniques such as (but not limited to): Bayesian statistics and modeling, linear/nonlinear regression, linear optimization, mixed integer programming, supervised/unsupervised learning, expert systems, network analysis, and neural networks
- Deeply understands not only how to use a technique but why it is or is not appropriate in a given situation, with available data, and for specific business needs
- Familiarity (or ability to become familiar) with privacy-preserving or identity protecting techniques and ways to discern bias in models that may impact recommendations regarding diverse and underserved populations
- Expertise in Python and/or R (Clojure welcome too!), and deep familiarity with corresponding analytics, visualization, and data processing libraries
- Capable SQL skills and ability to navigate and work with data from a variety of internal and external databases (e.g. Postgres), APIs, and management
- Understanding of the nuances of testing models and addressing scalability/accuracy of analytical processes in probabilistic systems
- Advanced degree in computer science, data science, mathematics, statistics, or other related field
- Relevant published or publicized professional or academic work such as open-source contributions, blog posts, or publications
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