**Data Science @Dream Sports:**Data Science at Dream Sports comprises seasoned data scientists thriving to drive value with data across all our initiatives. The team has developed state-of-the-art solutions for forecasting and optimization, data-driven risk prevention systems, Causal Inference and Recommender Systems to enhance product and user experience.
We are a team of Machine Learning Scientists and Research Scientists with a portfolio of projects ranges from production ML systems that we conceptualize, build, support and innovate upon, to longer term research projects with potential game-changing impact for Dream Sports.
This is a unique opportunity for highly motivated candidates to work on real-world applications of machine learning in the sports industry, with access to state-of-the-art resources, infrastructure, and data from multiple sources streaming from 250 million users and contributing to our collaboration with Columbia Dream Sports AI Innovation Center.
Your Role:
- Transforming problems into analysis plans for data telemetry, metrics, predictive modeling, reporting, and experimentation
- Applying or developing tools and pipelines to efficiently collect, clean, and prepare massive volumes of data for analysis
- Developing processes and tools that monitor, analyze and continuously improve model performance and data accuracy Leading in solving technical problems, creatively meeting product objectives and developing best practices
- Leading in solving technical problems, creatively meeting product objectives and developing best practices
- Developing predictive algorithms that analyze large scale datatsets and recommend data-driven solutions
- Monitoring the performance of Junior Data Scientists and provide practical guidance as needed
- Collaborating with stakeholders across Data Science, Engineering & Product Management teams to design and implement software solutions for scientific problems
Qualifiers:
- 6+ years of experience in a Machine Learning Scientist or a similar role
- Proven track record of successfully designing and implementing machine learning solutions in a production environment
- Experience in machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) and programming languages (Python, R)
