Vacuumlabs is a team of 200+ engineers and designers. Our track record shows dozens of state of the art solutions delivered to startups and companies of all sizes.
Here’s what you will do
As a data engineer, you will:
- Use data to solve a business problem or create data-centered solutions.
- Use the latest approaches along the whole data science pipeline from data acquisition, preparation, feature engineering to applying and deploying machine learning models.
- Discover actionable insights through exploratory data analyses.
- Visualize the results of your analyses to explain your insights.
- Work with software engineers and business staff to validate your insights in production
- Peer review your colleagues’ analyses to sustain high quality.
- Lead your junior colleagues if you are in a more experienced position.
Who do we look for
- Demonstrated analytical skills and ability to reason about data and its connection to real world applications
- Work or other tangible experience in a role involving data preparation, modeling, exploratory analysis, and deploying supervised and/or unsupervised machine learning algorithms
- Good knowledge of SQL and experience in SQL data modeling.
- Good knowledge of Python & SciPy ecosystem including Pandas, Numpy or Scikit-learn
- Basic knowledge of Linux
- Knowledge of version control systems (git)
- Experience building data visualizations
Examples of our work
- retail banking
- Built algorithms to precisely classify clients’ income streams to deliver transaction-based insights into a bank’s credit scoring process
- Predicted customer churn through building a holistic view of a bank’s clients financial context and their interactions with the bank
- Built cutting-edge daily-banking use cases and automated financial advisory to promote financial wellbeing
How we work
We always begin and keep the END in mind. We’re impact driven and we will not stop until we have exhausted all ways to maximize the impact of data on your operations. We don’t analyze for the sake of analysis.
The world is COMPLEX as is people’s behavior. We iterate endlessly to identify, understand, and capture its idiosyncrasies. We’ll reduce complexity but not at the expense of losing information value.
We believe in SIMPLICITY. We’ll opt for a simpler approach or a simpler solution if it gets the job done.
We believe in UNDERSTANDABILITY. If you’ll not understand the output of our work and its implications, it’s useless. We hate useless.