We are a series-A startup building perception systems for autonomy. We are based in the San Francisco Bay Area, funded by NEA (https://www.nea.com/), and our core team includes faculty entrepreneurs (Stanford and UC Santa Barbara) and industry veterans (Uber, Apple, Amazon Lab126, Rohde & Schwarz), who have successfully shepherded signal processing and machine learning innovations to large-scale software for location improvement and safety at Uber, led the development of state-of-the-art computer vision technologies that shipped over millions of Amazon devices, and delivered zero-to-one product experiences at Uber and Box. Our core product grew out of 5+ years of university R&D by our co-founders. You can find out more about us by visiting our website (https://www.plato.systems/) and our notion page (https://plato-systems.notion.site/Meet-Plato-Systems-82230c5f8b8e44f2ac12e7cdf65a0ad5).

Our mission and team expertise spans beyond software to advanced sensor systems, algorithms, embedded systems, signal processing, and machine learning. Our team is building and deploying edge software and cloud services for real-time customer facing products as well as internal big data tools. We look for people with a depth of expertise and experience in one of these areas, and with the intellectual curiosity for interacting with, learning from, and teaching world-class experts in areas outside their expertise.

We currently have a full-time opportunity in the area of data science. As a Senior Data Science Engineer, you will sit at the intersection of Data science, Engineering, and Product, & work collaboratively with different teams to transform data generated by our fleet of edge devices into data products that are consumable by our end customers.

Responsibilities

• Work closely with our product and engineering teams, and use any combination of statistical methods, time-series analysis, rule-based and other algorithms in order to generate insights, build compelling features, and draw value from rich data generated by our edge devices on customer sites.
• Work in a data-driven environment, drive process improvement, and work with the stakeholders to translate high-level business goals to working software solutions and customer-facing outputs.
• End-to-end ownership in terms of definition, development, evaluation,
  integration, test, documentation, and ensuring scalability /
  availability of deployed services.
• Develop and improve the current data architecture, data quality, monitoring and data availability.
• Write data transformations and data analytics algorithms in SQL / Python, and generate data visualizations and statistical analysis that can be readily consumed by customers
• Design, define, and implement metrics and dimensions to enable analysis and predictive modeling
• Act as a bridge between customer and engineering sides to provide feedback to the engineering team and request changes to the algorithms running on the edge device.
• Own, troubleshoot & resolve code defects.
• Prepare technical requirements and software design specifications.
• Deliver high quality work under tight deadlines.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, or a related engineering field.
• 4+ years experience
• Experience building production type software, leveraging basic OO design/development skills, and practicing solid documentation methods.
• Strong SQL and demonstrated proficiency in Python with hands-on data modeling and manipulation experience (Pandas, visualization techniques, statistical analysis) and familiarity with algorithm development cycle
• End-to-end experience with data, including querying, aggregation, analysis, and visualization
• Self-starter, motivated, responsible, innovative and technology-driven person who performs well both solo and as a team member
• A proactive problem solver that has great communications as well as project management skills to relay findings and solutions across technical and non-technical audiences

Preferred Qualifications

• Experience with one or more of time-series analysis, statistical analysis, clustering, anomaly detection, and computer vision fundamentals for a data product
• Prior demonstrated experience with productionizing and orchestrating algorithms / data science processing jobs, including exposure to one or more of databases, modern data technologies stack (e.g. Data Bricks, Google Big Query, Redshift, RDS, Snowflake, Superset), and infrastructure components (Airflow, etc).
• Strong skills in Git, Docker
• Experience with data visualization tools and packages
• Comfort in working with business teams to gather requirements and gain a deep understanding of varied datasets