Lead Data Scientist, Applied Machine Learning
With the advent of genomic sequencing, we can finally measure and process our genetic makeup. We now have more data than ever before, but providers often don't have the infrastructure or expertise required to easily extract the valuable insights that exist within this data. Here at Tempus we believe the greatest promise for the detection and treatment of cancer & other diseases lies in building a deep understanding of the interaction between molecular attributes and clinical treatment.
We're on a mission to redefine how patient data is used in a clinical setting. We are looking for data scientists who are passionate about applying state of the art machine learning techniques to the processing and analysis of vast amounts of clinical, molecular, and imaging data.
What You'll Do
- Collaborate with product, science, engineering, and business development teams to build the most advanced data platform in precision medicine
- Design and prototype novel data visualization and analysis tools and algorithms
- Wrangle and analyze large diverse sparse datasets, extract insights, and drive further research opportunities
- Interrogate analytical results for robustness, validity, and out of sample stability
- Document, summarize, and present your findings to a group of peers and stakeholders
- Provide technical leadership & expertise across multiple modeling projects
- Lead a small to medium sized team of applied data scientist, developing team members, and actively managing their individual performance
- Construct quarterly functionality & resourcing plans, balancing team objectives & business value
- Effectively identify, communicate, and mitigate potential cross-functional risks to team objectives
- Actively engage with multiple disparate stakeholders, and effectively manage competing requirements & priorities
Qualifications
- Degree in computer science, software engineering, statistics, machine learning, data science, bioinformatics or related technical field
- 5+ years full time industry experience building and validating predictive models on structured and/or unstructured data
- 3+ years industry experience leading small to mid sized data science and/or research teams
- Experience mentoring and/or technical leadership of team members
- Proven success in taking project(s) from initial idea to production deployment, and an understanding of the trade-offs required
- Proficient in Python, and SQL
- Proficient with the following: Pandas, NumPy, SciPy, Scikit-learn, Jupyter Notebooks
- Proficient with supervised and unsupervised machine learning algorithms, and ensemble methods, such as: K-Means, PCA, Regression, Neural Networks, Decision Trees, Gradient Boosting
- Experience with: Git, matplotlib, seaborn, HTML5, CSS3, JavaScript, D3, Plot.ly, Flask, Dask
- Experience working with clinical and/or genomic data
- Experience working in a Linux / Mac environment
- Thrive in a fast-paced environment and willing to shift priorities seamlessly
- Experience with communicating insights and presenting concepts to diverse audiences
Nice to Haves
- Kaggle.com competitions and/or kernels track record
- Experience with AWS architecture