At Relevon, we’re building the future of commerce.
We are an (ambitious) startup so:
- We are driven by passion. Working in a startup ain’t easy, but loving what you do is the key to meaningful work. We are passionate about helping businesses grow and that’s what pushes us forward everyday.
- We play hard, but we work even harder. Think of us as small guerilla team ready to tackle any roadblock.
- We find ways to make it happen. Thriving as a startup means being relentless at finding solutions. Looking for excuses won’t help us succeed, but action will.
We are looking for a Data Scientist with powerful background in data science to join our engineering team to analyze large data sets and build the right models, while writing and maintaining the production code. Our culture as a company is making customer data marketing and personalized content accessible to all kinds of ecommerce businesses.
Our future team member has a background in data science, statistics and machine learning and has done work ranging from exploratory analysis to training and deploying models. We use a wide variety of data mining and machine learning algorithms. The right candidate will have both a solid fundamental understanding and deep practical experience with at least a few modeling and machine learning techniques.
Our data science team is yet young and our new member can have a big impact on our trajectory and how we operate. He/She will be central to upfront research and shipping products that help our customers learn and grow from their data.
In a typical week as a Data Scientist at Relevon you’ll
- Identify appropriate models/algorithms to solve product requirements
- Meticulous experiment to evaluate and compare models
- Write internal and external facing documentation describing models and approaches
- Deploy models to production and maintain them
What we’re looking for
Don’t worry if you don’t meet 100% of these requirements. Your desire to learn and your ability to work in a forward-thinking, collaborative environment is just as important to us.
- Strong fundamental understanding and experience with at least some machine learning algorithms (e.g. regressions, decision trees, k-means clustering, neural networks).
- Understand Bayesian modeling techniques.
- Ability to analyse data and make rigorous statements about what can or cannot be concluded.
- Experience in designing and implementing model performance/validation assessments.
- Background in statistics and understand different distributions and the conditions under which they’re valid.
- Ability to code and use data science tools and packages.
- A measurable impact based on the models created.
- The desire to ship features powered by data science (proactivity in both upfront research and actually getting models into production at cloud scale).
- PhD or MS in Computer Science, Electrical Engineering, Applied Math, Statistics or other relevant quantitative disciplines, or equivalent industry experience
- Relevant coursework and experience in the fields of Machine Learning, Statistics, Optimization
- Deep understanding of statistical/probabilistic analysis and linear algebra
- At least one year of relevant industry experience, including internships
- Ability to write production-ready code
- Experience with Natural Language Processing, Deep Learning, Reinforcement Learning, Optimization
- Experience with ML at scale
- Experience with scale computing: Hadoop/Scala/Spark/Cassandra
- Experience with SQL