As the company's Data Scientist Lead:
You will be a key driver for Soho’s competitive edge in the field of Data and Artificial Intelligence analysis. This will be a fast-paced and collaborative role where you will work closely with the product, development, and business teams. You will be productising the data within our business both to create improved application algorithms as well as preparing the data for external consumption by third party clients. The data tools we use are include Google BigQuery, Postgres DB, SQL, AWS AI suite (e.g. rekognition), Algolia AI, Mode.com and several analytics/reporting tools.
- Play a lead role in the rollout/management of machine learning tools, modelling and data engineering planning with our development team
- Identify opportunities to leverage and productise the company’s data, and lead execution of data projects.
- Identify areas to improve data quality and be able to create pipelines or manipulate data.
- Build processes to extract, transform, and analyse data using a various tools especially SQL.
- Ad hoc analysis of data to support business decision making.
- Data visualisation using variety of tools e.g. MS Excel or mode.com to communicate Soho’s data to both internal (team) and external (clients/partners) audiences
- Take ownership of reporting/monitoring business metrics by setting up dashboards.
- Work with other teams (marketing, design, engineering) to drive overall business metrics
You are an ideal candidate if you have the following skills:
- Computer science or Engineering degree is required
- Knowledge of SQL is a must, for data transformations and analysis.
- Experience with various data/storage formats (CSV, XML, Excel, relational databases)
- Previous experience with ML based products and data engineering is essential
- Keen interest in data, and passion to problem-solve and think creatively.
- Attention to detail and thoroughness in reviewing data quality.
- Be able to effectively communicate data and insights in various visual formats, like charts, graphs, histograms, etc.
- Be able to work productively and autonomously in a fast-paced environment.
- Knowledge or experience in quantitative modeling and statistical approaches to data (e.g. statistic regression, predictive modeling, recommendation engines etc.).
- Knowledge or experience with engineering will be an advantage.