Mid Level Data Scientist Job in Everestek

Details

• Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc.
• Experience with common data science toolkits, such as R, Weka, NumPy, MatLab, etc. Excellence in at least one of these is highly desirable
• 3+ years of hands-on experience developing machine learning algorithms with Python
• Expert in Python, with knowledge of libraries such as NumPy, Pandas
• Excellent communication skills is a must
• Good applied statistics skills, such as distributions, statistical testing, regression, etc
• Experience with data visualisation tools, such as D3.js, GGplot, etc is a plus
• Proficiency in using query languages such as SQL, Hive, Pig is a plus
• Experience with NoSQL databases, such as MongoDB, Cassandra, HBase is a plus
• Software development experience with any programming language is a plus
• Knowledge of deploying machine learning solutions in cloud environments such as AWS is plus
• Data-oriented personality
• Excellent logical and problem solving skills

Job Role

• Selecting features, building and optimizing classifiers using machine learning techniques
• Data mining using state-of-the-art methods
• Develop and deploy machine learning algorithms in python
• Extending data from multiple sources of information when needed
• Enhancing data collection procedures to include information that is relevant for building analytic systems
• Sift and analyse data from multiple angles, looking for trends that highlight problems or opportunities
• Use strong business acumen, as well as an ability to communicate findings, and mine vast amounts of data for useful insights
• Use a combined knowledge of computer science and applications, modelling, statistics, analytics and maths to solve problems
• Processing, cleansing, and verifying the integrity of data used for analysis
• Doing ad-hoc analysis and presenting results in a clear manner
• Creating automated anomaly detection systems and constant tracking of its performance
• Make recommendations to adapt existing business strategies
• Constantly monitor how algorithms are working in production and present all findings to stakeholders
• Prepare and give detailed demos to key stakeholder