Experts, Data Science and Data Engineering

Locations Helsinki, Hyvinkää, Jyväskylä, Oulu, Seinäjoki, Tampere, Turku, Vaasa

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Data is the number one business driver in the 21st century, it gives us insights on improving operations and delivering personalized services to our customers. We at Wapice create state of the art AI powered solutions to enable our customer to reach their business goals and to stay ahead of the competition. We continuously look for new and innovative ways for our customers to extract insights from the large volumes of data they acquire. We provide our comprehensive services utilizing the latest computer vision, anomaly detection, prediction, natural language processing and data analytics technologies. We deploy our solutions to run reliably and seamlessly at the edge, cloud or local server platforms to accomplish high availability and performance requirements.

Data Scientist, Data Engineering and Edge AI positions

Experts needed for three different competence profiles: data engineering, edge AI, and data scientist. As Wapice expert you may concentrate on one of these fields or in the long run you may develop a competence profile covering skills on two or three above previously mentioned areas. Please read the following short descriptions of each specific role and related competence requirements to find out your suitability for the available expert positions.

As a Data Engineer you will work as a key contributor in building the success of our big data and data science projects. You know at least the basic principles of data processing. You are interested in the design of data platforms. Modernizing data warehousing and refining the data gathered from IoT devices are part of your tasks as well. You have solid knowledge of database design, SQL and star schema. You should have experience in building data pipelines, big data processes and you understand the challenges that a large amount of data may bring.

More specifically, you are familiar with couple of technologies listed below:

  • Python, SQL, Bash, Go, Java, Scala, C#
  • Data warehouse/data lake design and ETL processing
  • Data orchestration tools
  • Data processing tools
  • Relational databases
  • Non-relational data stores such as wide column store, document-storage, key-value storage
  • Cloud platforms (Azure, AWS, GCP from a data engineer’s perspective)
  • Docker

As an Edge AI expert you are familiar with edge architectures and edge calculation implementations covering entire DevOps pipeline, cloud integrations, analytics and utilizing machine learning models at the edge. You have experience in analysis of various sensor data in an embedded machine-learning environment. You are familiar with integrations of IoT device data to systems either in the cloud or at the edge. You understand how to design a solution around latency, bandwidth and cost considerations.

More specifically, you are familiar with couple of technologies listed below:

  • Linux
  • Nvidia Jetson Series, Edge TPU, Movidius, FPGA, ARM
  • OpenMV, TensorFlow Lite
  • AWS Greengrass, Azure IoT Edge, EdgeX Foundry
  • Docker, Kubernetes

As a Data Scientist you will be responsible for providing value from data by working in different customer projects. Work starts from understanding the customer's data together with customers´ domain experts and setting hypotheses leading towards valuable results. You will be constantly deepening your theoretical knowledge by testing the results of the latest scientific research in practice and developing your software development skills by designing and implementing production ready AI solutions.

There are large amount of different technologies and concepts applied in data analytics. We expect you to have experience or strong will to learn some of the following:

  • Python C/C++
  • Linear Algebra, Multivariable calculus, Statistics, Signal Processing, machine learning
  • Experience in processing of video, audio, text, multivariate data and time series
  • Applied knowledge of object detectors, classification, clustering, anomaly detection, prediction, NLP
  • TensorFlow, Keras, PyTorch, Sklearn, OpenCV, Numpy

Finally, find below the following work descriptions — written by your future colleagues — introducing the challenges, technologies and environments you could be working with:


My project assignment included research of computer vision system to locate and classify different defects from images, and implementation of the chosen method to production environment. Project was implemented using Python and its open source frameworks suitable for solving the problem such as NumPy, OpenCV and PyTorch. There were regular status meetings with the customer throughout the project, where current status was presented, and next steps were planned based on customer requests. Research stage of the project consisted of getting familiar with data and parsing it to a format that can be used in with semantic segmentation deep learning models. After that, the model architecture was designed, and model was trained from the scratch customer data only using Wapice's internal GPU resources. Lastly, the developed model was integrated to a web application for using it in production. For this purpose, a REST API and a responsive UI for operating and visualization purposes was designed and implemented. The application was deployed using Docker for server environment.


The data for this project consists of time series data collected by sensors that measure physical conditions of machines. The raw data, stored in a SQL database, needs preprocessing which includes data imputation, outlier detection, and aligning the database tables with different sampling frequencies and timestamps. The analysis itself consists of both traditional machine learning and fitting the data to a known physical model aiming at predicting the physical condition of the machine using raw sensor data. Data exploration through visual inspection plays an important role in this project. The analysis and visualizations have been implemented using open source Python libraries such as Pandas, Scikit-learn and Plotly. The project is conducted by a pair of Wapice data scientist working in a close cooperation with the customer's R&D unit.


I work as a data scientist in a customer-lead team consisting of data scientists, software developers and domain experts. My main responsibility is to develop algorithms running on an IoT device for analyzing sensor data on the edge using machine learning, linear algebra and statistics. I work in a close cooperation with a software developer from Wapice with whom I got to design the IoT device's software architecture for running the analyses. The initial implementation, analysis and testing are primarily done using Python and its open source libraries such as Cython, Pandas and Scipy but the optimized analyses running on the device are implemented in C++.

We offer you a unique opportunity to join our data scientists´ team to solve thrilling challenges in industrial domain, often using the state-of-the-art AI methods and practices.