17/Oct/2019

Job ID: dsid171019

INTERNET / eCOMMERCE

Data Scientist

 at JAKARTA 

#Data Science #Predictive Modeling #deep learning #Machine Learning #Analytics #Python #SQL  

Key Skills 

# Data science stacks (Python+DS libraries, SQL, R+DS libraries, H2O)

# Deep learning (RBM, CNN, RNN, LSTM)

 4-6 Year Work Ex.  

The Data Science team works towards creating unique solutions from building predictive models to optimizing processes for drivers, merchants, and customers.

 

As a data geek yourself, did you know that you can create #impactatscale through data? We have an ocean of data for you to dive into. And as a bonus, there are so many challenges ready to be tackled and you could be the one who turns it into limitless innovations.

 

With a deep focus of growth and efficiency, we are looking for smart, technically savvy, creative thinkers to help solve unique business problems!

Employer Background

Job Resposibilities

  • Use advanced analytics to extract insight from data, to build models from it, and to build the first prototype (MVP) if you are tasked with developing new machine learning based features.

  • Using the quantitative skills to drive/involve in product and feature discussions. Therefore while being an expert in machine learning and quantitative topics, one should possess a little bit of everything else: problem-solving skills of management consultants, skillsets of product analysts, data engineers, and machine learning engineers. This allows rapid prototyping to be iterated quickly without necessarily waiting for dedicated specialists.

Requirements

  •  Master in quantitative degrees (ML, Math, CS, etc.) or bachelor degree with proven track record in building an end-to-end data science project.

  • Minimum 1 year of experience in the data science field.

  • Hands-on knowledge in common data science stacks (Python+DS libraries, SQL, R+DS libraries, H2O, version control, etc.). Ability to frame business problems into data science problems and create MVP (Minimum Viable Product) of out it.

  • Comfortable with the collection of mathematical apparatus needed to get the job done: classic (logistic, SVM, RF, XGBoost, etc.) and deep learning (RBM, CNN, RNN, LSTM, etc.) algorithms, convex and non-convex optimization, Linear Algebra, etc.

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