Lead Data Scientist
Location Mountain View
Consultant Anne Ngo
Date posted September 27, 2016
Our client in the travel space is actively looking to bring on board a Lead Data Scientist.
- Designing algorithms for the Social Feed and the Local Search feature
- Work with the engineering team
- Able to work with production constraints and aware of performance trade-offs
- Ensure that the features are built in an iterative manner, and fit into the standard SDLC scrum model.
- Work with the engineering team to build the frameworks and tools required to design, prototype and test algorithms
- Foster a culture of data-driven decision making
- Strong background and hands-on experience in designing and implementing machine learning algorithms
- Self-driven and highly motivated
- Effectively communicate and align multiple stakeholders
- Build a strong, productive, and inclusive team culture by mentoring team members
- 10+ years of hands-on software development experience.
- 3-5 years of experience as a data scientist, preferably in a lead role.
- Strong programming experience in one or more of Python, Java, C++, C#.
- Hands on experience with the popular libraries and toolkits for machine learning and data visualization – R, NumPy, Pandas, Scikit-Learn, Matplotlib, MatLab, etc.
- Deep knowledge of algorithms for search relevance, ranking and recommendation systems.
- Hands-on experience with building the ranking component of a search engine using Solr or Elasticsearch is a big plus.
- Breadth of knowledge in supervised and unsupervised learning – regressions, decision trees, gradient boosting, clustering, etc.
- Able to quickly prototype machine learning algorithms and show potential impact via data visualizations, thereby guiding product roadmap.
- Able to collaborate with the engineering teams to take prototype implementations to production.
- Good understanding of large scale systems design, especially search systems.
- Master’s or PhD degree in Computer Science or a related field, with a focus on Machine Learning, Data Mining, Statistics or Information Retrieval.