Senior Machine Learning Engineer
Full Remote
This role is with a Syrinx Global eCommerce Partner
U.S. Citizens and those authorized to work in the U.S. are encouraged to apply. We are unable to sponsor at this time. No Corp to Corp.
- Develop quantitative models, leveraging machine learning and advanced data analysis techniques
- Own the full Data Science life-cycle from conception to prototyping, testing, deploying, and measuring its overall business value
- Coordinate, prepare, launch, and assess live experiments in order to measure the incremental impact of your own work and/or the work of partner teams
- Uncover deep insights hidden in our vast repository of raw data, and provide tactical guidance on how act on findings
- Drive adoption & utilization of your products across the organization in ways that drive real business value
- Architect and help define the required technical platforms that enable us to produce models at scale
Qualifications:
- 2+ years of experience in a quantitative or technical work environment, or an advanced degree (PhD) in quantitative field (e.g. mathematics, economics, computer science, statistics, engineering, physics, neuroscience, operations research etc.)
- Ability to effectively partner with cross-functional leads: strong communication skills, ability to synthesize conclusions for non-technical partners, and a desire to influence business decisions
- Ability to thrive in a dynamic environment where Data Science plays a key role in shaping business & technical priorities
- Machine Learning experience in a professional or advanced academic setting (e.g., supervised/unsupervised learning, recommendation systems, reinforcement learning, deep learning, etc.)
- High comfort level with Python (preferred), or with other programming languages. Experience with big data tools such as Hadoop, Spark, Presto, etc.
- Prior experience building scalable data processing pipelines with big data tools such as BigQuery, Spark, etc. Experience with Airflow and containerization (Docker) are nice to have
- Experience with multi-objective optimization is a bonus