Data Scientist (Machine Learning Engineer) at Paymish

Job Description

• We are looking for a Data Scientist that will help us discover the information hidden in vast amounts of data and help us make smarter decisions to deliver even better financial products
• Your primary focus will be in applying data mining techniques, doing statistical analysis, and building high quality prediction systems integrated with our products, and build system for automated fraud detection in financial transactions, and develop internal A/B testing procedures.

Responsibilities

• Selecting features, building and optimizing classifiers using machine learning techniques.
• Data mining using state-of-the-art methods.
• Extending company’s data with third party sources of information when needed.
• Enhancing and automating data collection procedures to include information that is relevant for building analytic systems.
• 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.
• Collaborate with engineering and product development teams.
• Analyze large amounts of information to discover trends and patterns.

Skills and Qualifications

• Bachelor's Degree in Computer Science, Engineering or relevant field; Master’s Degree in Data Science or another quantitative field is a plus.
• 3+ years experience as a Data Scientist, Machine Learning Engineer, or Data Analyst.
• 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, Pandas, NumPy, MatLab, etc. Excellence in at least one of these is highly desirable
• Experience with deep learning specifically in the areas of NLP, NLU, text analysis, and recommender systems.
• Great communication and presentation skills.
• Experience with cloud computing with AWS.
• Experience with data visualization tools, such as D3.js, GGplot, etc.
• Proficiency in using query languages such as SQL, Hive, Pig etc.
• Experience with NoSQL databases, such as MongoDB, Cassandra, HBase
• Experience with deep learning frameworks such as Pytorch, TensorFlow, and Keras.
• Good applied statistics skills, such as distributions, statistical testing, regression, etc.
• Good Knowledge of Python is compulsory.
• Data-oriented personality.