As a medior data scientist, you have good experience with generative and discriminative classification and regression, linear and non-linear dimensionality reduction, predictive analytics and time series modeling. You will be part of the Data Science Team and you are passionate about machine learning and data analysis. Job description
- You will design and implement state-of-the-art methods for both supervised and unsupervised learning, with a focus on sensor data such as gyroscope and accelerometer streams.
- Your knowledge of signal processing allows you to apply the necessary pre-processing steps such as band pass filtering, down sampling while anti-aliasing, Fourier or Cepstrum coefficient extraction and spectrogram modeling.
- You have experience with temporal modeling techniques, both for discrete state spaces (e.g. hidden Markov models and dynamic Bayesian networks) and for continuous state spaces (e.g. Kalman and particle filtering). You have provable experience with generative (e.g. Gaussian Mixture Models) and discriminative (e.g. Random Forest) classification techniques.
- You have a strong theoretical and mathematical background and are able to reason about machine learning peculiarities in order to answer questions such as: Is a Bayesian classifier with Gaussian likelihoods and priors the same as a Euclidean distance classifier if equal and diagonal covariance matrices are used? When should we choose a linear model as opposed to a more flexible nonlinear model in the face of a high dimensional feature space? How do we avoid aliasing when processing and down sampling high frequency sensor data?
- You will help the team to improve upon current methods and models. You have a practical mindset and are able to bring these models into a production environment. As such, you have extensive experience with Python or another relevant programming language.
- Your programming experience will allow you to closely collaborate with our Data engineering team to improve our models and push them through our release process.