Data Science Technical Lead
La Haus, Colombia
Job Description
La Haus
A Latin American family’s most valuable asset is its home. Geographic and financial mobility is stagnated due to low liquidity, the absence of robust platforms and marketplaces, as well as fragmented and unprofessional service providers. Our mission is to radically transform the residential industry, through technology, data and world-class service: provide asset and geographical mobility to Latin American homes. To fulfill this mission, we are building a technological platform that assists the home buyer through their journey, ranging from the initial search, to financing to the final purchase.
The role
We are looking for someone to lead the Data Centered Solutions (DCS) development team at La Haus. The team currently consists of three members and we expect it to grow significantly in the near future, by hiring at least two more members. The role is instrumental in defining the ways in which advanced predictive models are used within the various product verticals to achieve the company’s strategic and tactical goals. The ideal candidate will have strong technical skills and experience in the development and deployment of machine learning and data intensive algorithms to production environments, in both online and batch modes. More important than that will be the candidate's soft and managerial skills and their ability to effectively communicate and empathize with the various product verticals and their managers (PMs), C-level executives and the company in general. In particular, the candidate should be creative, proactive and propositive, willing to experiment and to fail fast, to ultimately bring Data Science and ML at La Haus to the next level.
Mission
The DCS team mission is to provide our home buyers with an experience that they would perceive as intelligent. In other words, customers should get a sense that the data we collect about them and their search preferences are handled in an intelligent way to improve their home-buying experience.
To this aim the team has developed models to:
- Recommend the best alternatives of properties for customers, based either on explicit search preferences or preferences implied by navigation behavior.
- Rank the order of appearance of properties on our search result pages (SRPs) to optimize user experience / maximize click-through-rate to property detail pages.
- Suggest properties similar to a given property
- Suggest search filters in real time on the SRP (not yet deployed)
- Match a user with the LH real estate agent most likely to close a deal with them (not deployed)
- Score registered users according to their buying intention
- Score a property picture according to its visual appeal
- Classify a property picture according to which kind of room it shows
The team strives to constantly refine and improve these models and part of this role’s mission is to direct those improvements.
Responsibilities:
- Perform all managerial duties in the DCS team.
- Search for and hire new DCS team members.
- Contribute to the continuous improvement of the team’s technical knowledge.
- Actively participate in architectural decisions concerning integration of data products and models into the commercial product.
- Supervise the development and deployment of statistical and advanced data centric models.
- Formulate requirements to the data engineering team about the content and format of various data sources that will feed into the team’s created models.
- Be in constant and close communication with various product managers to gain a deep understanding of product and business needs and how they could be satisfied with data science and ML tools.
- Be proactive, suggest simple, effective solutions that are easy to implement and maintain.
- Be propositive: suggest improvements in data capturing, representation and storing procedures.
- Know and use methodologies for the development of predictive models CRISP-DM and SEMMA, paying special attention to aspects related to business understanding and integration.
- Stay updated on the latest developments and trends in the fields of ML, DL, Deep RL, as well as the technologies and architectures used for their deployment.
- Be empathetic towards PMs and the commercial side of the company, but firm in pushing forward a technical, data driven approach.
- Be didactic in the divulgation of what ML and predictive analytics can and cannot do.
- Ask smart questions, assume risks and defend new ideas.
Requirements
Skills:
- Communication:
- Assertive and effective communication, with a didactic disposition.
- Able to distill the essentials of technical ideas to make them accessible to other less technical member