ABOUT THE TEAM

We're a highly capable team of engineers and data scientists, working together to build a first-of-a-kind commercial decision engine for the hospitality industry. Our products are based on proven methods in data science and machine learning applied in novel ways. We have been recently acquired by US-based start-up FLYR, who have raised $250M to date (most recently on a Series C). We are still operating independently and will grow from 40 to 100 people by mid 2023.  

The scientific challenges that we are facing at Pace are immense, bringing modern multivariate parametric and non-parametric modeling techniques to commercial operations research. Our research covers a wide range of topics: from time series analysis to Bayesian statistics, Monte Carlo simulations and reinforcement learning. 

We keep our entire codebase in Python and are moving towards a real-time microservices infrastructure. The work of the data science team is based on end-to-end ownership of models and reproducibility. Model improvements are driven by demonstrability of their impact on our clients in our sophisticated validation framework that mimics CI/CD for software projects. 

A bit more about our data science stack, which successful candidates will work with:

• Python 3.9, Pandas, SciPy, Scikit-learn, NumPy

• Tensorflow, PyMC3, Dask and Gurobi to run our models

• Looker for our internal analytics

• Cloud-native technologies and DevOps practices for experimentation and seamless production operations

THE ROLE

We are looking for Data Scientists to join the pricing and forecasting algorithms teams. 

In the pricing algorithms team, Data Scientists work on a variety of challenging issues, but first and foremost on using our client’s and external data to understand real time market forces that are affecting their demand and react optimally to those forces. You will help expand our algorithm’s capabilities with a focus on using Bayesian statistics, incorporating new data sources and enabling new algorithm-driven product features. 

In the forecasting algorithms team, Data Scientists serve our customers and our pricing algorithms team through developing best-in-class highly granular forecasting algorithms of demand. You will be instrumental in expanding the datasets and ML algorithm classes that we use, as well as providing insights into algorithm behavior that can be communicated to customers. 

In both teams, as part of the science organization at Pace which will be over 25 people by mid 2023, above all, you’ll help drive a fundamental technology-driven shift in the hospitality industry using insights from Operations Research and advanced statistical modeling techniques prevalent in a variety of fields, from finance to physics and computer science, in a collaborative environment. 

WHAT WE OFFER

• Benchmarked, market-tracking salary

• Generous options package on our parent company, a US post-series-C start up

• A company of kind people who care about each other

• Unlimited holiday policy 

• Very flexible working arrangements (remote first with the possibility of going to our physical offices in Prague or London when desired)

• Fun quarterly offsites (after all, we work in hospitality!) and ad-hoc face-to-face meetings as needed

• Generous and forward-thinking approach to learning and development

• An opportunity to make a big impact in a well-funded, growing, industry-transforming B2B SaaS company

ABOUT YOU

A person who’s likely to succeed and thrive in this role will recognize themselves in some of the following areas. Don’t worry if you don’t catch them all, this is not a game of Pokemon!

• You like building models and understanding the math behind them. You will build proprietary models, based on your knowledge of optimization, forecasting and statistics. Understanding how the model works is the only way to understand how to improve it, or how to reuse it elsewhere within our systems. 

• You have built models based on modern ML techniques. Attention models, transformers, CNNs, they all play an important role in modern time series forecasting. While explainability is an important part of our models, you will help us expand our forecasting capabilities in client-facing applications.

• You like writing production-ready software in Python to implement your models. At Pace we believe that the most efficient way to design a model that will work in production is to let the model designers do the implementation and validation. Python (and relevant libraries like pandas) will be your best ally in that exercise.

• You are meticulous about the quality of your code. Code not tested is most likely wrong. You care about your code being correctly implemented and understandable. Unit testing, validation pipelines and monitoring pipelines are all helpful tools to make sure your code is behaving as expected, and you are keen on working on them. 

• You want to succeed as a team. We hope you enjoy working in a cross-functional team, and understand that everyone from developers and data scientists all the way to customer success play an equally important role in a company.

• You take ownership of your work. The team will rely on your expertise and collaboration to  solve our algorithm design and implementation problems. This role is suited to someone who likes to work with autonomy within a team of skillful peers.

NEXT STEPS

You can start the application process by clicking on the yellow button on the top right of this page.

You’ll answer some questions that are related to your day-to-day job. After the job closes, your answers will go through our de-biased sift process: all answers will be anonymised, randomised and then scored by members of our team.

Expected duration of this application process: 2-4 weeks

We are an equal opportunities employer

We are committed to a policy of Equal Employment Opportunity and are determined to ensure that no applicant or employee receives less favourable treatment on the grounds of gender, age, disability, religion, belief, sexual orientation, marital status, or race, or is disadvantaged by conditions or requirements which cannot be shown to be justifiable.