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Jr. Data Scientist & Engineer

It’s tough for businesses to attract upcoming data Scientists and Engineers with the skills to drive your organisation towards data dominance. That’s why we created our junior development program. We attract and select data Scientists and Engineers at the top of their class, find the right match for your business needs, and provide them with an unparalleled level of expert guidance and technical training from our senior data science and data engineering team as they take on your biggest problems. You receive an immediate boost to your data science and engineering projects, plus the option to hire after the one year junior development program has concluded.

Don’t search for a unicorn, grow it.

          We select top talent from data courses at leading universities with 1-2 years work experience.

          Candidates work 4 days per week with clients and spend every Friday improving their skills through our proven development program.

          You get a well-rounded candidate with the resources of the Netherlands’ top data analytics & AI team at their back.

Profiles junior Data Engineer & Scientist

Name: Janis

Education: Msc Information Technology

Placement: Shipping Technology 

Major projects:

BlackBox Pro (ongoing – shipping technology website; team of 4/5):

  • Real-time Data Pipeline assisted with the development of a real-time data pipeline which is parsing raw data from ship on-board sensors. Data is both being streamed to our applications on the cloud and to our local on-board application platform. We then deliver data and provide applications like collision detection and data dashboard to our clients.
  • Local App platformhelped develop an app platform that can extract useful information from on-board sensors and/or can take control over the ship. Apps in development – collision detection, semi-autonomous shipping.
  • Cloud infrastructure –  Part of a team that developed cloud infrastructure on Google Cloud Platform (GCP):
    • Dashboard – data replay and data presentation;
    • Storage solution – data storage and archiving;
    • Database system for material data handling;
    • Data processing;
    • Cloud Gateway.
  • Maintenance serviceshelped develop cloud infrastructure to maintain both systems on the cloud and on ships:
    • VPN connection;
    • Ship health monitoring (connectivity and hardware health);
    • Update rollout system.
  • Support – physical assembly of servers; machine installation and configuration; mobile network configuration and setup.

Stack of tools and languages I’ve learned and used: 

    • Google Cloud PlatformApp Engine, Cloud Functions, Datastore, Storage, PubSub, Dataflow, Google Kubernetes Engine, Cloud Build, Container Registry;
    • General tools: Redis, OpenVPN, Ansible, Docker (and docker compose), Kubernetes, Flask, Alembic;
    • Languages: Python, Javascript.

Name: Ilse

Education: Msc Data Science

Placement: HEMA

Major projects: 

  • Salesbridge – Developed a model to give insights in the sales of the past week. What affected the sales using weather, stock availability, newness ect. 
    • Collecting data
    • Preprocessing
    • Modelling
    • Deployment
  • Budget forecastingDeveloped a model to forecast the weekly sales for 2020 on different levels using Prophet time series model.
    • For each country
    • Online & offline
    • Per category level
  • HR employee survey  – Analysing the text part of the employee survey by using text mining for the different countries and units in HEMA. Also added sentiment. 
  • Cloud platform – Part of the team that decides how the HEMA data platform will be formed. 
    • Data migration from data warehouse to the cloud
    • Data science environment on the platform 
    • Data pipelines on the platform 
    • Connection by API’s 
  • Single customer view – Created insight on each customer using the customer card. 
    • Created all queries / ETL jobs to gather data from different sources
    • Inserted data into Google big query so it can be used in Adds
    • Inserted data into AWS so it can be used with the email tooling

Stack of tools and languages I’ve learned and used: 

    • General tools: Docker, Flask, powerBI, Microstrategy, AWS sagemaker, Google BigQuery
    • Languages: Python, SQL