Sr. Machine Learning Engineer / Data Scientist (Risk) at Turo
San Francisco, CA, US

About Us

Turo is a peer-to-peer car sharing marketplace where guests can book any car they want, wherever they want, from a global community of local car owners.

We are a group of creative and mission-focused data genies, environmentalists and car enthusiasts who want to put the world’s 1B+ cars to better use. We do this by using data to serve our customers' delightful experiences by matching them with the right car for their next adventure while keeping our marketplace safe.

We’ve grown the business by over ten times in the past two years and raised over $200 million from investors like Daimler, SK Holdings, Kleiner Perkins, Google Ventures, August Capital and Shasta Ventures.

About You

You are a creative, rigorous senior machine learning engineer / data scientist who is excited to solve meaningful business problems. You are comfortable with a wide range of machine learning techniques with the ability to creatively apply them to our problems. You are proficient in translating unstructured business problems into an abstract mathematical framework and making intelligent approximations to proposal practical and scalable solutions.

You have a passion for consumer products, shipping code, and conducting data focused experiments. You enjoy developing models that will have an enormous impact on our next chapter. And you are excited to play a key role in crafting the product from inception to implementation and beyond.

Responsibilities

  • Work closely with product engineers, other data scientists, and product managers to develop and deploy algorithms and drive key product and modeling decisions. Work with the data science manager to ensure the models developed are production ready.
  • Lead the end-to-end process of conceptualizing, developing, productizing, deploying and analyzing machine learning models in the Cost of Insurance (Risk) vertical.
  • Present your insights and suggestions to audiences of all levels in the company.
  • Dedicatedly perform explorations of data and algorithms to improve our models. Research the best metrics and experiments to measure model performance.
  • Mentor junior team members to help them elevate to the next level.

Required Qualifications

  • MS in a science/engineering field with 5+ years (or PhD with 2+ years) of relevant industry experience.
  • Strongly self-motivated with a keen pair of eyes for details. A natural desire to learn and innovate with like-minded colleagues.
  • Deep understanding of mathematical methods in machine learning. Demonstrable experience applying them to real data problems such as classification, regression, recommendation system, and time series analysis.
  • Fluent in a production-ready programming language such as Python. Experience in deploying and scaling models into a production environment as microservices.
  • Fluent in data analytics tools, e.g. Jupyter notebooks, scikit-learn and PyTorch/Keras in Python (preferred), or equivalent tools in R.
  • Fluent in SQL or SQL-like query languages; Experience in efficiently query large datasets from data warehouses and data lakes.

Preferred Qualifications

  • Experience in working with risks such as insurance or actuarial science, etc. is strongly preferred.
  • Experience in the latest ML techniques, such as neural network.
  • Experience with AWS SageMaker or ETL framework such as Airflow. Experience with Spark, and/or Hive. 

Benefits

  • Competitive salary and meaningful equity
  • Employer paid medical, dental, and vision insurance
  • Apple equipment of your choice
  • Four weeks paid time off, 11 paid holidays, volunteer time off, generous parental leave
  • Weekly catered lunch with a fully-stocked kitchen
  • Company-sponsored happy hours and team events
  • Turo owner matching and vehicle reimbursement program
  • Turo travel credit every month
  • Engineering education allowance every month

Turo is an equal opportunity employer and values diversity at our company. We don't discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.