Job Duties and Responsibilities
About the position
Our mission is to build the next generation, web scale platform for Sling TV. Our environment is…
- Highly elastic
- Based on some of the latest and greatest cloud native technologies
- Very fast paced
Your team will be…
- Enabling a proper enterprise Data Lake in AWS
- Building models and tools to get value out of the mass amounts of data we have in our environment
- Enabling the best, most personalized and resilient customer experience possible
In order to be successful in this role, you will need to be…
- Highly motivated, driven & hard working
- Able to lead a team of 3-5 into the world of data science
- Not afraid to fail and comfortable working independently and with a team
- Comfortable working with massive datasets in real time and batch processing with superior analytics skills
- Comfortable talking to and working with Senior Executives
- Apply data mining techniques, do statistical analysis, and build high quality prediction systems integrated with our product. Doing ad-hoc analysis and presenting results in a clear manner.
- Processing, cleansing, and verifying the integrity of data used for analysis
- Enhancing data collection procedures to include information that is relevant for building analytic systems
- Data mining using state-of-the-art methods. Create automated anomaly detection systems and constant tracking of its performance.
- A team player. We have a great group of diverse folks working together in harmony. Big egos and "super heroes" need not apply.
- Real-time machine learning
Skills - Experience and Requirements
A successful Data Science Lead will:
- Be available to work onsite out of our American Fork, UT or Englewood, CO offices
- Have a 4-year college degree in Computer Science / Information Technology, master's degree and or a PHd is preferredor equivalent professional experience
- 10+ years professional enterprise experience
- Open to occasional travel for quarterly planning meetings and or other key workshops
Here are some of the key technologies that make up our environment. While we do not expect you to have a detailed understanding of each, the more of these you are familiar with the better.
- ELK Stack / HDFS / Hadoop / Hive
- AWS Big Data Tools: S3, Kinesis, Red Shift, Athena
- Java, Python, R, SQL
- Machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, h20.ai, etc.
- Artificial Intelligence
- Predictive Analytics
- Tableau or other visualization tools
- Kafka / Confluent
- CI / CD and Cloud Native Computing (Docker, Kubernetes, Consul, Vault)