Sr. Enterprise Analytics Data Scientist
Knox Medical is a premier medical cannabis company, with cultivation, processing and retail locations in Florida, Texas, Pennsylvania, Puerto Rico and Canada. Our purpose is to compassionately provide patients access to the highest level of medical cannabis in an unrivaled, professional healthcare environment, with outstanding customer service. Patient care always comes first at Knox Medical and our standards, practices, and performance is unmatched.
We are looking for leaders who want to work alongside great talent and create a positive work experience for their team. We are focused on continuous improvements, which creates development opportunities, engaged feedback, and increased contributions. Our goal is to inspire, lead, trust and deliver on what we say we are going to do.
Find out more at www.knoxmedical.com. Follow us on LinkedIn
The incumbent will be a part of a new Enterprise Analytics and Data function, whose mission is to transform Cansortium Holdings into a data driven organization. As an enterprise data scientist, you will be working in flexible, multidisciplinary and self-driven teams using agile methods with open source products on BIG DATA enterprise platform to provide actionable insights for the strategic business challenges of the rapidly changing multinational company. Some of the key responsibilities include:
- Managing software development projects at various levels
- Reviewing/planning clinical studies across multiple CNS programs, and conducting analyses related to time series modeling, supervised and unsupervised learning techniques, Bayesian methodologies, and others
- Provide statistical and modeling support for planning and implementing clinical and real-world evidence studies/programs to meet the value/evidence needs of payers, health systems, clinicians, and patients.
- Provide solutions of clinical and economic utility by leveraging internal, external and real-world data sources.
- Guide evidence-based product development (digital/non-digital) by extracting therapeutic intelligence from such data sources
- Use data sciences to optimize product development and raw material processing procedures
- Use and application of a variety of statistical techniques to assist with the molecular and phenotypic data analysis in genetics research.
- Analyze large-scale biological data to produce high-confidence insights to drive breeding decisions.
- Design experiments and implement the strategy for statistical analysis of plant breeding and experimental data with collaborators/researchers.
- Integrate genomic, phenotypic and environmental data to address core scientific questions and hypotheses.
- Drive science focused solutions to create digital ad capabilities and products to enhance value and efficiency for researchers, collaborators and customers.
- Identify, acquire, and engineer feature data sets and pipelines with potential to address customer needs.
- Apply machine learning, statistical or mechanistic models and other computational approaches to extract insights from datasets in complex agronomic systems.
- Develop, prototype, and implement data pipeline and software solutions with engineering and production teams.
- Collaborate with scientists from multiple domains in solution/experiment design and analyses.
- Conduct and communicate results of research on data science approaches to improve decisions, add value to services, extend or improve in-house models and algorithms, and contribute to the advancement of these ideas into the marketplace.
- Masters or PhD in Applied Statistics or Mathematics or any similar field requiring data analysis, statistics, computer sciences, machine learning, algorithms design or process optimization
- Strong data modeling skills such as neural networks, multivariate analysis (MVA), time series, regression and nonlinear models, support vector machines, deep learning etc.
- Experience with common statistical concepts, approaches, packages and libraries, programming and doing statistical analysis in Python, SAS or other statistical software
- Experience with genomic tools and analyses (association mapping, QTL analysis, phasing, etc.)
- Experience in model testing such as back-testing, MC simulations, ANOVA, model selection
- -Knowledge of pharmaceutical modeling tools such as NONMEM, Monolix, Phoenix NMLE, etc.
- Ability of presenting statistical conclusions to business leaders through data story telling
- Knowledge of Hadoop ecosystem, including Apache SPARK, Hive etc., as the successful candidate will be working on Hortonworks Data Platform on Amazon Web Services (AWS)
- Knowledge in object-oriented or functional programming.
- Building mathematical statistical models on top of big data architectures to gain deep insights into opportunities to improve the strategic decision-making process
- Translating statistical complex information to key stakeholders
- Connecting with data science communities to explore innovative opportunities.
This job description in no way implies that the duties listed here are the only ones the employee can be required to perform. The employee is expected to perform other tasks, duties and training as dictated by his supervisors.
KNOX Medical is an equal opportunity employer and we welcome applications from all backgrounds regardless of race, color, religion, sex, ancestry, age, marital status, sexual orientation, gender identity, disability or any other classification protected by law.
Please note: Only shortlisted candidates will be contacted.