Company
Doorstead is on a mission to revolutionize the property management industry with modern technology and data-driven pricing.
The Team
The Data Team at Doorstead works with the Product and Engineering teams to design, experiment, and deploy machine learning products to provide industry-leading service to our owners and tenants.
The Role
We’re seeking a mid-level Data Scientist to join our small but growing Data Team. You should be able to be a project lead, making key decisions through all phases of a project, including exploratory analyses, prototyping, testing, and presenting your solution to business stakeholders. The ideal candidate will have the mathematical and statistical expertise you’d expect, but a natural curiosity and creative mind. As you mine, interpret, and clean our data, we will rely on you to ask questions, connect the dots, and uncover opportunities that lie hidden within—while keeping an understanding of the business case in mind. You should be a problem solver and a fast learner. Since we are a small company in the rapid-growth stage you should also have a keen sense of when to sacrifice predictive accuracy for a more rapid and simple solution.
Responsibilities
- Leading projects from start to finish
- Working with our Engineering team to ensure Data Science team is getting the proper support
- Selecting features, building and optimizing classifiers using machine learning techniques
- Data mining using state-of-the-art methods
- Extending company's data with third-party sources of information when needed
- Enhancing data collection procedures to include information that is relevant for building analytic systems
- Processing, cleansing, and verifying the integrity of data used for analysis
- Doing ad-hoc analysis and presenting results in a clear manner
Skills and Qualifications
Required
- Bachelor's degree and 3-5 years of professional experience
- Great communication skills
- Experience with data visualization tools, such as Plotly, Seaborn, matplotlib, ggplot2
- Proficiency in using query languages such as SQL
- Excellent scripting skills (preferably with Python and Pandas toolkit)
- Excellent applied statistics skills, such as distributions, statistical testing, regression, etc.
- Data-oriented personality
Nice-to-have
- Excellent understanding of machine learning techniques and algorithms
- Familiarity with Airflow, MLFlow, Catboost
- Familiarity with object-oriented programming in Python (classes, inheritance, etc.)