Trax is looking for a Data Scientist to join its core R&D activity. This person will be joining our CPG group and act as an important player in driving research, design, development, and delivery of a novel platform for the world of consumer goods on our unique data.
The ideal candidate will be a highly-motivated, analytic person, familiar with state-of-the-art technologies, and capable of learning our business in detail. He or she will work in a dynamic environment undergoing rapid growth and will be expected to take ownership of optimizing learning-based models to guarantee the data quality of our innovative solutions
- working closely with our engineering teams building innovative solutions based on machine learning
- Use cutting edge machine learning algorithms, statistics, and business intelligence for optimizing existing models as well as the development of new ones.
- Apply creativity to research and explore new applications from our current and future data.
- Take an active role in the realization and implementation of analytic projects and streamlining them during their development process.
- Build strong interfaces with R&D, product, delivery, and operations team within the solution group and apply data-driven decision making in these fields.
- Good understanding of data and its limitation: sampling, frequency, manipulation and cleaning techniques to name a few
- 4+ ears of hands-on experience in the Python Data Science Toolbox (Pandas, NumPy, SKlearn) from either work, academia or open-source projects
- 4+ years of experience with either SQL based RDBMS or NoSQL
- 4+ Working experience with large data sets and good acquaintance with the Big Data ecosystem (Spark, MapReduce, Hive, Pig, etc) –
- Hands-on experience in applying statistics and machine learning methods on real-world data
- Proven visualization and presentation skills – an advantage
- Working Experience in Cloud-based environments – an advantage
- Hands-on experience with time series anomaly detection - an advantage.
- M.Sc./PhD in a relevant field with strong quantitative skills