Job Description
- Formulate novel data-driven solutions and monitor key performance indicators toward furthering our company strategies
- Develop and productionize machine learning models for our controlled substance compliance product to solve meaningful challenges in preventing drug diversion
- Partner with our product teams and key customers to identify and tackle high-impact opportunities in the pharmaceutical supply chain
- Serve as a liaison between engineering, data, and non-technical audiences, including critiquing requirements, informing roadmaps, assessing risk, and presenting during customer-facing meetings
- Build data team culture and improve data literacy within the organization
- Stay informed about developments in Data Science and adjacent fields to ensure that outputs are always relevant
- Raise the skill level of the entire analytics team through the creation of outstanding work, introduction of better processes, and mentoring of newer data scientists
- Degree or equivalent years of experience in data science, statistics, computer science, or similar quantitative field
- Ability to develop and productionize code in Python to serve API endpoints (e.g. Flask, FastAPI)
- Deep understanding of statistical models as well as both supervised and unsupervised machine learning methods (e.g. regression, time-series analysis, XGBoost, K-Means, Clustering)
- Experience with Amazon Web Services tools (e.g. Sagemaker, Glue, DMS, Lambdas)
- Master-level understanding of SQL (e.g. MySQL, PostGres, Presto)
- Prior track record of a customer-friendly approach in presenting information and analysis for non-technical audiencesÂ
- Excellent verbal and written communication skills with an ability to advocate technical solutions to engineering teams as well as business stakeholders
- Master’s degree or PhD in math and/or statistics centric field of study
- Experience on a data team of a similar size (<10)
- Experience working with protected healthcare data (e.g. ADM, EHR) and pharmaceutical supply chain logistics (e.g. invoicing, cost optimization)
- Experience working with ETL tools (e.g. Airflow, DBT) and data lake/warehouse infrastructure (e.g. Redshift, Spark)
- Experience with MLOps including deployment, CI/CD Pipelines, and monitoring
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Date Posted
01/10/2024
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