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Data Scientists at Zenefits work on problems that are important to the company’s mission. Major challenges include developing systems and models that provide deep insights to customers about their employee population and help them make the right decision. Examples of insights could be predictive analysis on employee related attrition and churn analysis, comparative analysis of compensation info that allows employers to hire effectively.
Data scientists are expected to apply machine learning and deep learning techniques to derive insights for customers of Zenefits. In addition to developing the company’s data products, data scientists provide decision support analysis for many teams across the organization including product development, sales, marketing, finance and strategy. Data scale ranges from small data sets to large multi-terabyte information in distributed database systems.
- Lead and develop AI/ML driven data projects from end-to-end encompassing design to technical implementation, debugging, testing, and iteration
- Partner and work cross-functionally with data infrastructure engineers, data analysts, product managers, and engineers to identify analytics opportunities and their execution.
- Selecting features, building and optimizing classifiers using advanced machine learning techniques to enhance predictive analytics offering to customers
- Information retrieval and data mining using state-of-the-art methods
- Extending company’s data with third party sources of information when needed
- Enhancing data collection procedures to include information that is relevant for building analytic systems
- Processing, cleansing, and verifying the integrity of data used for analysis
- Doing ad-hoc analysis and presenting results in a clear manner
- Regularly write high quality code, perform code reviews, and produce excellent peer reviews on projects prior to shipping
- Evaluate and experiment with new technologies and tools prior to wider adoption by the team
- Operate at high degrees of competency and sophistication in statistics, machine learning, and computer science
- Graduate degree (preferred Masters/Phd) in quantitative discipline such as computer science, applied mathematics, statistics, physics, operations research, management of information systems, engineering, economics, social sciences or equivalent
- 5+ years of industry experiences developing data-driven products and deploying in global environments
- History of applied data mining using structured/unstructured data, supervised/unsupervised machine learning, and statistical modeling to solve business problem
- Deep understanding of statistical learning concepts such as linear and logistic regression, kernel methods, SVMs, Bayesian learning, probabilistic graphical models, and neural networks
- Strong SQL skills preferred
- Comfortable with very large data sets as well as big data platforms (e.g AWS, Hadoop ecosystem, Hive, Spark, Presto, Vertica, Greenplum, etc)
- Demonstrable proficiency in coding (Python or R preferred) and passion to build
- Experience in deep learning techniques a plus
- Industrial experience using data science tools and packages (e.g. Scikit-learn, TensorFlow, nltk, Weka)
- Exposure to cloud computing platforms such as Amazon Web Services, Microsoft Azure, or Google Cloud Platform
- Self-driven individual, demonstrating continuous learning and creativity, and is naturally collaborative
- Excellent verbal communications, including the ability to clearly and concisely articulate complex concepts to both technical and non-technical collaborators