About
We are looking for a Data Scientist that is eager to explore the world of Big Data and stretch the capabilities of our tools. We want you to take charge and bring new ideas, new projects and new technologies to the table.
Relocation package
Job rotation
Learning through Arnia Academy
Flexibile working hours
Performance bonuses
Attractive projects
Medical benefits
Trainings
Competitive compensation package
Referral program
International work experience

Project:
We want you to embark in the driving seat of the Advanced Analytics future and build the most exciting and innovating Machine Learning models. You will wonder in the world of open-source programming languages like Python and R and integrate yourself in the Hadoop unlimited possibilities combined with the power of the Spark framework.

Requirements:
• A degree with strong fundamentals in mathematics or computer science or other related fields;
• 3+ years experience working in a data science role in a commercial environment (financial and banking would be a plus);
• Very good knowledge about statistics including distributions, correlations, probabilities, hypothesis testing etc;
• Experience with machine learning algorithms and techniques (dimensionality reduction, classification, clustering, regression, time series, association rules etc);
• In-depth knowledge of industry standard techniques for data processing, data management, data delivery, data mining, analysis and reporting (SQL);
• Very good experience with Python including Sklearn/Statsmodels/Pandas/Numpy/Scipy/etc (any other experience in using a scripting programming would be a plus);
• Experience with visualization tools (Power BI/Plotly/GGplot/etc );
• Understand the big data ecosystem and its major components (HDFS, YARN, MapReduce, Spark,Pig, Hive, Kafka etc.);
• Experience with ML/Ops, CI/CD pipelines;
• High analytical abilities and curiosity for finding hidden insights in data.

Responsibilities:
• Machine Learning driven models development;
• Business inquire regarding future model developments;
• Develop and maintain reports regarding the model performances;
• Data Sources discovery and manipulation;
• Production integration for developed models;
• Results communication by translating insights into business value.

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General application

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