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Job Description
- Selecting features, building, and optimizing models using machine learning
techniques - Data mining using state-of-the-art methods
- Enhancing 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 - Provide clear insights that help define strategy, and facilitate speed and accuracy of
decision-making, and ultimately make a positive impact on the business. - Work in the opportunity, investigate, and explore phases of product development to
identify the biggest and most valuable consumer needs and motivations - Have a strong toolkit for quantitative and qualitative methods to apply in problem-
solving - Translate business questions into learning plans and then lead, develop, design and
conduct primary research studies to enable the synthesis of data to develop insights. - Ensure effective communication of market data, including synthesizing them into
insights for action - Manage third-party vendors to ensure learning objectives are met, and quality research
standards are maintained - Find opportunities to develop and champion the use of new research methods to
address unique research challenges and evolving trends - Remain abreast of best practices within the discipline and research methods
employed, including the latest evolution of approaches as the industry develops - Coordinate multiple tasks simultaneously, meeting deadlines and working
productively and efficiently under pressure
Job Requirements
- Strong applied mathematical and statistical skills regardless of the tools.
- Excellent written and verbal communication skills for coordinating across teams.
- Knowledge of a variety of machine learning techniques (clustering, decision tree
learning, artificial neural networks, etc.) and their real-world advantages/drawbacks - 0-3 years in the field of information and business intelligence systems.
- Data-oriented personality.
- Good applied statistics skills, such as distributions, statistical testing, regression, etc.
- Good understanding of data mart schemas, and OLAP tools.
- Good understanding of data visualization concepts and tools.
- Good understanding of using query languages such as SQL.
- Broad understanding of databases (e.g. Oracle PL/SQL, Mongo), and high-
the performance or distributed processing (e.g. Hadoop, Spark) - Deep understanding of probability, statistics and machine learning theory.
- Experience with Python, Machine learning libraries, and data mining.
- Good scripting and programming skills Python, R Data-oriented personality.
- Must have a good understanding of machine/deep learning techniques and algorithms
(SVM, Naive Bayes, Decision Forests, Neural Networks, etc.) - Applied experience with machine learning on large datasets.
- Demonstrated skills in selecting the right statistical tools given a data analysis
problem. - Experience with statistical software (e.g., R, Julia, MATLAB, pandas) and database
languages (e.g., SQL). - Hands on experience with machine learning frameworks such as TensorFlow, Keras,
etc... and techniques such as supervised machine learning, decision trees, logistic
regression etc... - Advanced level in Microsoft Excel
- Build, test and deliver new analytics/models using the new data sets as required to
underpin use case. - Excellent understanding of machine learning techniques and algorithms such as k-
NN, Naive Bayes, SVM, Decision Forests, etc. Experience with common data science
toolkits.