You will be crucial in supporting our business by creating valuable, actionable insights about the data, and communicating your findings to the business. You will work with various stakeholders to determine how to use business data for business solutions/insights.
Responsibilities
* Analyze and derive value from data through the application methods such as mathematics, statistics, computer science, machine learning and data visualization. In this role you will also formulate hypotheses and test them using math, statistics, visualization and predictive modeling.
* Understand business challenges, create valuable actionable insights about the data, and communicate your findings to the business. After that you will work with stakeholders to determine how to use business data for business solutions/insights.
* Enable data-driven decision making by creating custom models or prototypes from trends or patterns discerned and by underscoring implications. Coordinate with other technical/functional teams to implement models and monitor results.
* Apply mathematical, statistical, predictive modelling or machine-learning techniques and with sensitivity to the limitations of the techniques. Select, acquire and integrate data for analysis. Develop data hypotheses and methods, train and evaluate analytics models, share insights and findings and continue to iterate with additional data.
* Develop processes, techniques, and tools to analyze and monitor model performance while ensuring data accuracy.
* Evaluate the need for analytics, assess the problems to be solved and what internal or external data sources to use or acquire. Specify and apply appropriate mathematical, statistical, predictive modelling or machine-learning techniques to analyze data, generate insights, create value and support decision making.
* Contribute to exploration and experimentation in data visualization and manage reviews of the benefits and value of analytics techniques and tools and recommend improvements.
Qualifications
* Strong quantitative skillset with experience in statistics and linear algebra.
* A natural inclination toward solving complex problems.
* Knowledge/experience with statistical programming languages including R, Python, SQL, etc., to process data and gain insights from it.
* Knowledge of machine learning techniques including decision-tree learning, clustering, artificial neural networks, etc., and their pros and cons.
* Knowledge and experience in advanced statistical techniques and concepts including regression, distribution properties, statistical testing, etc.
* Good communication skills to promote cross-team collaboration.
* Multilingual coding knowledge/experience: Java, JavaScript, C, C++, etc.
* Experience/knowledge in statistics and data mining techniques including random forest, GLM/regression, social network analysis, text mining, etc. Ability to use data visualization tools to showcase data for stakeholders.
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