Data Science Engineer

We're building an Enterprise AI platform focusing on deep customer understanding, modelling and emotionally intelligent customer engagement. If you have a startup mentality and a desire to help some of the world’s largest organizations leverage the latest ML / AI technologies we want to hear from you.

Genus AI is an international company with offices in San Francisco, London and Vilnius. Come join us to change the world in a meaningful way!

Data scientists at Genus AI work closely with product and engineering teams and clients to provide a high-quality service that exceeds expectations as well as researching and developing innovative machine learning product ideas.

We are looking for an experienced data scientist with an engineering mindset to help us.

You will...

  • Work with our clients to understand their requirements, business questions and build models and data pipelines.
  • Research and develop innovative machine learning based product ideas.
  • Work with commercial and product teams and build efficient data pipelines to enable data-driven decisions internally.
  • Develop data scientists on the team, helping them advance in their careers.
  • Improve data science standards, tooling, and processes.

You may be fit for this role if you...

  • Have a good command of written and spoken English.
  • Have a good understanding of security best practices and basic cybersecurity hygiene.
  • Have a strong background in data science or data engineering or both.
  • Have 3+ years of experience in data science or data engineering role, with a focus on data analysis or machine learning research.
  • Knowledge of Python (scikit-learn) and SQL.
  • Have familiarity with H2O.ai.
  • Enjoy sharing knowledge, teaching and mentoring young peers.
  • Can explain complex statistical models in basic language.

Benefits

  • 2000-3000 EUR net monthly salary or more.
  • A personal budget of 1,000EUR per year for learning courses of your choice, conferences, books, etc.
  • 25 work days of holidays.
  • Competitive salary.
  • All the tech you need to do your job.
  • Unique opportunities to grow professionally and as part of the team.

What is it like in engineering at Genus AI

First and foremost we want to be proud of our team, the work we do together, to learn from one another and set an example and be driving force in AI application in positive ways.

We follow Gall's Law when we build systems - start simple and gradually improve. We build systems that are not algorithmically pure, but easy to change, adapt and improve. We are fans of "Choose Boring First" approach and try to be smart about the technologies we pick.

We are optimizing our engineering towards sustainable speed. We encourage the release of small iterative changes, that are fast to review and verify. This gives us a significant speed in trying out various ideas and getting feedback fast.

We review everything that goes into production, both to improve the quality of our code and to share knowledge. We use a subtly different review workflow that contributes a lot to our productivity and speed.

We automate things when it makes the most sense.

In all of the above, we strive to be keep a high-security standard. We use a set of tools to automate compliance checks. We do regular software upgrades, review our software dependencies and regularly organise pen-testing. We enforce good basic cybersecurity hygiene wherever possible and expect every team member to support this.

Languages, Frameworks and Tools you will encounter at Genus AI

Most of our codebase is written in Python (3.6+). A basic understanding and ability to follow PHP and/or Java code would be a plus. We understand that languages can be learnt, therefore we are more interested in your engineering ability.

Our infrastructure is built on top of AWS and we use CloudFormation for Infrastructure-as-a-Code, and Ansible with custom plugins for automation.

We use Phabricator for most of our engineering and product workflows.

We use Django 2 with MariaDB for our client facing platform.

We use Jinja2 templates and vanilla JavaScript for the simple front-end side. There is room to grow this into something more modern.

We use Jupyterlab with Python, Scikit-learn, Tensorflow and H2O.ai for data science workloads. As we plan on doing more R&D there is an opportunity for validating any other cutting edge ML tools and frameworks.

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